Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 124 appraisals
Medical physics
Reference-Free large language model agents for physician-guided radiotherapy treatment planning
BACKGROUND: Large language models (LLMs) have recently demonstrated exceptional capabilities, offering the potential to streamline workflows and enhance efficiency across diverse tasks. However, their application in domain-specific areas, such as radiotherapy treatment planning, remains challenging due to the lack of publicly available, specialized knowledge required for these tasks. PURPOSE: To investigate the critical architectural and functional components necessary to adapt an off-the-shelf large language model into a clinically viable planning agent for inverse treatment planning in intensity-modulated radiation therapy (IMRT). MATERIALS/METHODS: Twenty head-and-neck (HN) cancer patients who received IMRT at our institution were retrospectively collected under IRB approval. The LLM agent was implemented to directly interact with the clinical treatment planning system (TPS) to iteratively extract intermediate plan states and propose new constraint values to guide inverse optimization. Its decision-making was informed by real-time plan evaluations and prior optimization outcomes, enabling adaptive refinement of planning strategies across iterations. The agent was equipped with three core capabilities: comprehension of clinical planning objectives, contextual understanding of the optimization environment, and arithmetic proficiency for quantitative reasoning. Treatment planning was conducted in a reference-free inference setting, wherein the LLM operated without prior exposure to manually generated treatment plans and without any fine-tuning or task-specific training. LLM-generated treatment plans were compared with clinically approved plans created by certified dosimetrists. Key dosimetric endpoints were evaluated and statistically analyzed. Paired comparisons between LLM-generated and clinical plans were performed using the Wilcoxon signed-rank test. RESULTS: LLM-generated plans achieved comparable organ-at-risk (OAR) sparing relative to clinical plans, while demonstrating improved hot spot control (Dmax: 106.5% vs. 108.8%, p < 0.05) and superior conformity (conformity index: 1.18 vs. 1.39, p < 0.05 for boost PTV; 1.82 vs. 1.88, p = 0.47 for primary PTV). CONCLUSIONS: This study demonstrates the feasibility of a reference-free, LLM-driven workflow for automated IMRT treatment planning in a commercial TPS. The proposed approach provides a generalizable solution that could reduce planning variability and support broader adoption of AI-based planning strategies.
2 Aug 2026
Read appraisal →Cancer medicine
Applications of Artificial Intelligence in Cancer Diagnosis and Treatment
Driven by changes in lifestyle and environmental factors, the global incidence of cancer is steadily increasing, which has established it as a leading cause of mortality worldwide. The current paradigm for cancer diagnosis and treatment relies on conventional methods, such as imaging, endoscopy, and tissue biopsy, which present significant limitations regarding sensitivity in early screening, diagnostic specificity, and personalized treatment. Consequently, the development of more efficient and accurate technologies remains a major objective in modern oncology research, and artificial intelligence (AI) has emerged as a particularly promising solution. Through machine learning and deep learning algorithms, AI is reshaping cancer care by enabling automated detection of minute lesions during screening and quantitative analysis of pathological features for diagnosis. It may also advance tumor theranostics through multimodal data integration for treatment stratification, response prediction, and image-guided or targeted therapeutic decision-making, whereas providing data-driven recommendations for personalized treatment. Despite these prospects, medical AI development faces several key issues, including data bias, model explainability, clinical reliability and generalizability, emerging limitations of foundation models and generative AI, and regulatory and ethical issues that need to be addressed. By reviewing recent advances in AI across screening, diagnosis, theranostics, and treatment, we aim to clarify where these methods are already useful, where evidence remains limited, and why closer collaboration among clinicians, engineers, and data scientists is needed for clinical translation. We hope this review serves as a practical reference for researchers and clinicians evaluating how AI may be integrated into oncology in a more standardized, clinically responsible way.
1 Aug 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.
BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the current literature has not demonstrated improved patient outcomes based on the decisions of the MDT conferences. AIM: We aimed to evaluate how four different decision-support modalities impacted the decision-making process and the internal discussions in the MDTs in a multicenter simulation study, with a focus on user perceptions. METHODS: Four colorectal cancer centers with MDTs participated. We performed four simulations in each center. Each simulation used a different decision-support tool: (1) Current standard, (2) Current standard plus a prediction model, (3) A structured data presentation tool, and (4) A structured data presentation tool plus the prediction model. Clinician- and model-estimated risks were compared, the treatment suggestions from each site were compared, questionnaires about user perceptions were conducted after Simulations 2, 3, and 4 using a Google Form link, and a semi-structured interview was conducted at each site after the last simulation. RESULTS: Similar distributions of risk groups between clinicians and models were found; however, distinct discrepancies in predictions arose, particularly with higher-risk patients, highlighting the need for standardization for more complex clinical cases. The primary perceived benefit of decision support was increased standardization of care, independent of the individual physicians' personal views. However, participants emphasized the necessity of clinician autonomy to overrule tool suggestions when identifying clinical nuances not captured by the model. CONCLUSIONS: The colorectal cancer MDTs expressed a positive view regarding the use of prediction models and other forms of decision-support in their workflow. While clinicians and prediction models had similar risk score distributions, they diverged in the assessment of specific individual patients.
1 Aug 2026
Read appraisal →Physics in medicine and biology
Anatomically adaptive feature-wise linear modulation for deep learning-based low-dose CT denoising
Objective.Low-dose computed tomography (LDCT) reduces radiation dose but, introduces heterogeneous noise due to different photon attenuation based on anatomical tissue. Most deep learning techniques assume uniform noise in LDCT and perform equal noise removal across different regions, leading to sub-optimal performance across different tissues. This work aims to design a physics-based framework that explicitly models region-dependent noise characteristics to improve LDCT noise removal.Approach.We propose an anatomically adaptive noise reduction framework. The proposed anatomically adaptive feature-wise linear modulation (FiLM) model consists of a U-Net architecture that integrates two complementary units: the global FiLM unit in the encoder, which modifies features globally based on global image statistics to remove overall noise, and the local FiLM unit in the decoder, which modifies features locally based on the type of anatomical tissue. This dual design enables the modeling of overall image noise characteristics in addition to the removal of local noise associated with each anatomical tissue.Main results.The model performance was evaluated using AAPM-Mayo Clinic LDCT dataset, and the trained model tested on the TCIA dataset. The proposed model outperformed all competing methods. Local noise analysis showed that noise removal was consistent across different anatomical regions, achieving 37.14% in the lung, 48.75% in soft tissue, and 35.18% in bone. Visual results also confirmed significant improvements in noise removal, preservation of structural details, and reduction of non-residual distortions. Furthermore, the model demonstrated its ability to generalize under domain shift.Significance.This work presents a framework for anatomically adapted noise removal by linking feature modification with the physical properties of noise in LDCT through a dual-modulation process for both general and tissue-related noise. The model achieves a balance between noise removal and preservation of anatomical detail, making it a robust approach to LDCT noise removal.
28 July 2026
Read appraisal →Journal of radiation research
Prioritizing radiotherapy infrastructure in depopulating societies: a 2050 blueprint from Akita, Japan.
In super-aged and depopulating societies, repeated travel requirements for radiotherapy (RT) can amplify geographic inequities in access to advanced techniques. The aim of this study was to determine optimal placement of intensity-modulated radiotherapy (IMRT)-capable facilities in Akita Prefecture-a leading-edge case of Japan's demographic transition-using p-median optimization under population projections through 2050. Using 1-km mesh population data for 2020 and official projections for 2030, 2040 and 2050, we calculated one-way automobile travel times from each mesh centroid to RT facilities with the Open Source Routing Machine based on OpenStreetMap road data. A 60-min travel-time threshold defined access coverage. IMRT-capable facilities were identified from official insurance-designation lists. Optimal placement was determined using a population-weighted p-median model, with the number of facilities prespecified as one per 200 000 population. In 2020, 10 external-beam RT facilities provided near-universal 60-min coverage (99.99%; mean 12.8 min), whereas the single IMRT-capable facility covered only 70.3% (uncovered 285 273; mean 42.5 min). Optimized placement with five IMRT-capable facilities increased coverage to 99.99% (uncovered 89) and reduced mean travel time to 14.2 min. Under population projections, optimized placement maintained >99% coverage through 2050 with three facilities, and three core locations were consistently selected across all time horizons. These temporally stable core locations can serve as priority targets for long-term IMRT infrastructure investment in aging and depopulating regions.
27 July 2026
Read appraisal →Journal of radiation research
A model-based selection of oral cancer patient for passive scattering proton beam therapy
BACKGROUND AND PURPOSE: To identify patients with oral cancer (OC) who benefit from proton therapy versus photon therapy using a model-based approach (MBA) to reduce late adverse events. MATERIAL/METHODS: Passive scattering proton beam therapy (PSPT) and volumetric modulated arc therapy (VMAT) plans were established for 40 patients with OC, consisting of a dose of 70 Gy (relative biological effectiveness) in 35 fractions. At least 95% of the planning target volume received 95% of the prescribed dose, and the dose to the organs at risk was restricted to be as low as possible. Normal tissue complication probability (NTCP) was used to estimate the risk of late adverse events, including dysgeusia, xerostomia and osteoradionecrosis of the mandible. Patients who were considered as candidates for PSPT were selected if the NTCP difference between both plans (ΔNTCP) exceeded the threshold defined in the Dutch national indication protocol. RESULTS: PSPT reduced the doses to the spinal cord, pharynx, oral cavity and contralateral salivary glands compared with VMAT. PSPT was also superior for the risk evaluation of each late adverse event calculated by incorporating dose metrics and patient baseline risk factors. For the MBA using ΔNTCP and the thresholds, PSPT was indicated in 24 of the 40 cases, and the remaining 16 cases were considered to be equivalent to VMAT. CONCLUSION: MBA was used to select OC cases that were candidates for PSPT. Compared with VMAT, the risk of expected late adverse events was significantly reduced with PSPT, and 60% of patients were considered eligible for PSPT.
26 July 2026
Read appraisal →Medicine
Survival benefit of concurrent beta-blocker use in cancer patients treated with radiotherapy: A systematic review and meta-analysis
BACKGROUND: Both preclinical and retrospective studies have implicated β-adrenergic signaling in cancer progression, leading to interest in β-blockers (BBs) as adjunctive anticancer agents. There is evidence that they may enhance the sensitivity of cancer cells to radiotherapy and other conventional therapies. We conducted a systematic review and meta-analysis to explore the radiosensitizing effects of BBs. METHODS: PubMed, the Cochrane Library, Embase, Web of Science, the China National Knowledge Infrastructure, the China biology medicine database, Wan fang Data, and the VIP database were searched for articles in both English and Chinese from the establishment of the databases to September 8, 2025, to identify studies comparing outcomes according to beta-blocker use (yes vs no) in patients with solid tumors treated with radiotherapy. The primary endpoint was overall Survival (OS), Secondary objectives included 1, 2, 5-Year Survival, disease-free survival, distant metastasis-free survival, locoregional progression-free survival and progression-free survival (PFS). RESULTS: 8 studies (3165 patients), including 7 retrospective cohort studies and 1 randomized controlled trial, were analyzed. The most common cancer was non-small cell lung cancer (n = 5). BB use was associated with significantly improved OS (hazard ratio [HR] 0.73, 95% confidence interval: 0.64-0.83). Benefits were also observed for disease-free survival (HR 0.69) and Distant Metastasis-Free Survival (HR 0.62), indicating reduced recurrence and metastasis. CONCLUSION: In this meta-analysis, the use of BBs during or around radiotherapy may be associated with longer OS in cancer patients, suggesting that it may enhance the efficacy of radiotherapy and provide a survival benefit. Alternatively, the effect of β-blockers on survival may shift from a nonspecific effect to an intriguing cancer-specific effect over time. Beta-blockers are an intriguing option to explore in prospective studies of patients with solid tumors undergoing radiotherapy.
26 July 2026
Read appraisal →Head and neck pathology
A Deep Learning AI Model for Histopathological Diagnosis and Grading of Mucoepidermoid Carcinoma of Salivary Glands
PURPOSE: This study aims to evaluate the effectiveness of deep learning AI model in the diagnosis and grading of mucoepidermoid carcinoma. METHODS: We developed a multiple instance learning based-deep learning model which consisted of two modules. One to perform slide-level classification of whole slide images into mucoepidermoid carcinoma and non-mucoepidermoid carcinoma classes and the second one to perform grading into low, intermediate, and high grades. A total of 194 whole slide images were included in the study. Of these, 92 corresponded to mucoepidermoid carcinoma, while the remaining 102 represented non-mucoepidermoid carcinoma cases. RESULTS: Our results showed that the classification model achieved 90.62% accuracy, 90.62% macro F1 score, 90.60% weighted F1 score, 91.67% macro precision, 91.18% macro recall. The grading model achieved an accuracy of 73.3%, weighted F1-score of 73.9%, weighted precision of 76.1% and weighted recall of 73.3%. CONCLUSION: The proposed deep learning classification model achieved promising performance in differentiating mucoepidermoid carcinoma cases. However, the grading model achieved an overall accuracy of 73.3%. Thus, depending on AI solely is still questionable, however it can be used to augment the work of the pathologist.
22 July 2026
Read appraisal →Neurosurgical review
Stereotactic radiosurgery for treatment-refractory obsessive-compulsive disorder: a systematic review of targets, targeting methods, lesion geometry, and clinical outcomes.
BACKGROUND: Stereotactic radiosurgery (SRS) is a noninvasive option for carefully selected patients with treatment-refractory obsessive-compulsive disorder (OCD), but the literature is heterogeneous in targets, targeting methods, lesion geometry, and outcomes. METHODS: PRISMA-guided systematic review of PubMed, Scopus, and Web of Science. Full-text human studies of radiosurgical capsulotomy for treatment-refractory OCD were included. Narrative synthesis was performed across technical and clinical domains. When ≥3 comparable studies reported the same endpoint, exploratory random-effects single-arm meta-analyses were performed. RESULTS: Twenty-one OCD-specific studies were included (1999-2024). The anterior limb of the internal capsule (ALIC), particularly the ventral ALIC, was the dominant target. Technique and dose reporting varied across cohorts, with commonly reported maximum doses spanning approximately 120-180 Gy, often using 4-mm collimation and single- or double-shot paradigms. Earlier reports primarily used anatomical MRI/coordinate-based targeting, whereas newer studies increasingly incorporated lesion mapping, tractography, and network-based analyses. Eleven cohorts were eligible for quantitative pooling. The pooled clinical response rate was 57.1% (95% CI, 49.1%-64.9%; I²=21.7%), and pooled remission was 36.8% (95% CI, 26.9%-47.9%; I²=0%). Severe delayed radiation-related injury was uncommon but present (6.4%, 95% CI, 3.1%-12.8%; I²=0%). Mechanistic studies suggested that lesion topography and pathway engagement may be more important than lesion size alone. CONCLUSIONS: SRS is a meaningful option for treatment-refractory OCD. The ventral ALIC remains the principal target, but outcomes likely depend on precise target identification and lesion placement. Standardized reporting and prospective evaluation of pathway-informed targeting are needed.
19 July 2026
Read appraisal →Breast cancer research and treatment
Extended applications of chest wall perforator flaps in breast surgery: case series and systematic literature review
PURPOSE: Chest wall perforator flaps (CWPFs) are integral to modern oncoplastic breast surgery, offering volume replacement without muscle sacrifice. While their role in partial breast reconstruction is established, data on extended and non-conventional applications remain limited. This study aims to synthesize current clinical experience and delineate the extended applications of chest wall perforator flaps beyond conventional oncoplastic contexts. METHODS: This study combined a prospective institutional case series from two tertiary centers (Singapore and UK) with a systematic literature review of PubMed, Embase, Web of Science, and Cochrane databases. Eligible studies described CWPF use for extended oncologic, reconstructive, or aesthetic indications. Data were synthesized descriptively according to PRISMA guidelines. RESULTS: Six women underwent LICAP-based reconstruction for complex indications including skin-replacing excisions, implant salvage, chronic sinus closure, and total autologous reconstruction with adjunct lipofilling. All flaps were fully viable with good contour restoration and symmetry, and adjuvant therapy proceeded without delay. The systematic review identified 38 studies (≈ 324 patients, 349 flaps). CWPFs, most commonly LICAP, LTAP, and TDAP, were used for defects following oncologic resection, radiotherapy injury, implant failure, or post-bariatric contour correction. Overall flap success exceeded 95%, with major complications under 5%. Donor-site morbidity was minimal, with high aesthetic satisfaction (> 90%). CONCLUSIONS: CWPFs represent muscle-sparing reconstructive options whose utility may extend beyond conventional oncoplastic indications; this descriptive series supports their feasibility and short-term outcomes across a range of selected clinical settings, suggesting that CWPFs warrant further consideration in the management of complex breast and chest wall defects.
17 July 2026
Read appraisal →Health care management science
An online algorithm for integrated scheduling of pre-treatment and treatment appointments in radiotherapy using deep reinforcement learning
Scheduling patients for radiotherapy within the oncology department is vital for ensuring timely and effective treatment. The patient's therapy consists of a sequence of pre-treatment consultations, i.e. preliminary investigations to define the required treatment, followed by the radiotherapy treatment sessions on linear accelerators. Timely scheduling of pre-treatment consultations is crucial to prevent unnecessary delays in radiotherapy treatment, thereby impacting patient health. Therefore, we suggest integrating both scheduling problems. We employ a Reinforcement Learning-based scheduling technique to solve the multi-appointment scheduling problem in an online fashion. We design a reward function comprising three components to guide the agent towards learning an effective scheduling policy involving multiple appointments. Additionally, we compare our proposed model with benchmark online heuristics. We further provide insight into the reward function design, which steers the agent to learn a feasible policy that leads to high-quality solutions. Experiments show that our algorithm outperforms the benchmark heuristics.
12 July 2026
Read appraisal →Physics in medicine and biology
Four-dimensional on-beam computed tomography reconstruction using projection-difference images
Purpose.Accurate verification of delivered dose requires anatomical information during the treatment beam delivery. However, conventional cone-beam computed tomography (CBCT) is typically acquired prior to beam delivery and fails to account for intrafractional tumor shift/drift. This study aims to develop a 4D on-beam computed tomography (4D OBCT) framework to capture anatomical states during irradiation.Materials and methods.A thoracic phantom with rigid tumor motion was used to develop a conformal arc treatment plan. During delivery, the target was displaced by 1 cm superior to its planned position to simulate a baseline shift, and megavoltage transmission data were acquired using an electronic portal imaging device. Transmission data corresponding to the planned tumor position were obtained from the planning CT (pCT) via Monte Carlo simulation. Volumetric difference images were reconstructed from the differences between the measured and simulated transmission data for each phase. Subsequently, the images were superimposed onto the corresponding phases of the 4D pCT to obtain 4D OBCT. For evaluation, we compared 4D OBCT with 4D pCT (ground-truth) and 4D CBCT which were acquired after the target displacement. A dosimetric performance was evaluated by recalculating 4D dose distributions on the three images and comparing in dose-volume histograms and gamma indices.Results.4D OBCT achieved lower root mean square error than 4D CBCT in both the lung (58.46 HU vs 74.37 HU) and chest wall regions (23.78 HU vs 66.84 HU). This quantitative accuracy was also reflected in the dosimetric comparison. Under 2%/2 mm and 1%/1 mm criteria, 4D OBCT achieved gamma pass rates of 99.76% and 99.08%, respectively, whereas 4D CBCT yielded 72.50% and 57.77%, respectively.Conclusions.The proposed 4D OBCT framework successfully reconstructed accurate phase-resolved images of the target during irradiation. By providing high-resolution volumetric images that reflect intrafractional motion, 4D OBCT could provide a robust solution for precise post-delivery dosimetric verification and adaptation.
12 July 2026
Read appraisal →Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
A systematic review of long-term outcomes (>10-year follow-up) of paediatric craniopharyngioma
PURPOSE: Adamantinomatous craniopharyngiomas (ACP) in children are rare benign tumours. Relatively few publications have addressed the long-term impact of this tumour and its treatment. We undertake a systemic review of the existing literature aiming to define the outcome of ACP beyond 10 years' follow-up. METHODS: Ovid MEDLINE, Embase and Google Scholar were used to search the literature. The search criteria were children (aged under 18 at diagnosis), any intervention for and outcomes of ACP. Publications with less than 10 years' follow-up and single case reports were excluded. RESULTS: Twenty-one studies were included reporting on 1152 children, with a mean age of 8.8 years at diagnosis. Mean follow-up was 14 years. The most common presenting symptom was headache. The mean overall survival at 5, 10, 15 and 20 years post-diagnosis was 91.6%, 84.4%, 77.5% and 68.0%, respectively. In children undergoing gross total resection only or radiotherapy only, survival was higher than with combined treatments. Seventy-five percent of children had endocrine dysfunction after intervention; the most prevalent was hypothyroidism (81.9%). The prevalence of endocrinopathies was similar across all treatment groups. Lower QoL was reported than the general population, predominantly related to the effects of hypothalamic injury. One in ten patients suffered a late complication related to radiotherapy with vasculopathy being the most prevalent. CONCLUSIONS: The long-term morbidity and mortality for paediatric ACP remain high. The impact from hypothalamic injury cannot be understated and is likely the cause for this finding. As surgical treatment advances, less hypothalamic injury may arise and improved outcomes may follow. Gross total resection, if felt achievable without hypothalamic injury, should form the mainstay of treatment due to the late radiotherapy-related complications that arise.
10 July 2026
Read appraisal →Journal of neuro-oncology
Feasibility evaluation of tumor treating fields for brainstem gliomas
PURPOSE: Tumor treating fields (TTFields) are FDA-approved for supratentorial glioblastoma, but feasibility in infratentorial tumors remains poorly defined. This simulation study evaluated TTFields dose in brainstem gliomas using patient-specific modeling with scalp-only transducer arrays. METHODS: MRI and CT imaging from seven patients with brainstem gliomas were used for TTFields planning with MAXPOINT® (Novocure, Switzerland). Clinical target volume (CTV) was defined as enhancing tumor on T1 post-contrast MRI (Gross tumor volume, GTV) plus a 3 mm peritumoral expansion. The platform optimized scalp-only array layouts, and finite element calculations generated maps of local minimum field intensity (LMiFI, V/cm) and local minimum power density (LMiPD, mW/cm³). Values were compared against a standard, unplanned layout using one-sided paired t-tests, with LMiFI ≥ 1.0 V/cm as a therapeutic reference. RESULTS: MAXPOINT-optimized layouts achieved significantly higher LMiFI than the standard layout across all regions (all p ≤ 0.019). GTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.019). CTV median LMiFI was 1.1 vs. 1.0 V/cm (p = 0.002). Brainstem median LMiFI was 1.3 vs. 1.1 V/cm (p = 0.002). Posterior fossa showed the largest difference: median LMiFI 1.5 vs. 1.2 V/cm (p < 0.001). All optimized LMiFI values met or exceeded the therapeutic reference. LMiPD was also significantly higher with MAXPOINT® across all regions (all p ≤ 0.006). CONCLUSION: This simulation study demonstrates that patient-specific modeling can achieve favorable TTFields intensities in brainstem gliomas using scalp-only arrays, supporting prospective clinical evaluation for infratentorial tumors.
8 July 2026
Read appraisal →Radiation protection dosimetry
Impact of neutron sieve therapy for deep site in boron neutron capture therapy using Monte Carlo calculation
Recent Boron neutron capture therapy (BNCT) has shifted to accelerator-based systems. One of them is a compact neutron generator developed using silicon carbide (SiC) semiconductors by Fukushima SiC Applied Technology Co. However, neutron penetration remains a critical challenge for treating deep-seated tumors. This study investigated the potential of neutron sieve therapy, originally developed to enhance the depth dose distribution in X-ray therapy, to improve BNCT dose distribution via Monte Carlo simulations based on the Fukushima SiC BNCT system. Dose distribution changes were investigated using polyethylene and 6LiF-plastic in block and sieve shapes. The results show that the maximum thermal neutron fluence depth was shallower (deeper) with polyethylene (6LiF-plastic) filters. The sieve filter moderately altered the dose distribution compared to the block filter due to density differences. The maximum dose point shifted 8 mm deeper using a sieve filter composed of 6LiF-plastic and polyethylene, with a 5.5% increase at 20 mm depth in neutron fluence at the same skin dose.
8 July 2026
Read appraisal →Biomedical physics & engineering express
A verification-oriented review of reaction-enhanced radiotherapy: linking nuclear data, transport modeling, and measurable observables
Reaction-enhanced radiotherapy (RER) aims to improve the therapeutic ratio of particle therapy by exploiting isotope-specific nuclear reactions that generate localized high-linear energy transfer secondary radiation. Absorbed dose remains the central metric for radiotherapy planning and evaluation; however, RER introduces additional verification requirements related to nuclear-reaction data, mixed-field transport, isotope delivery, and measurable secondary-field observables. This review frames RER as a verification-oriented physics problem built on three interdependent pillars: covariance-aware nuclear data, high-fidelity Monte Carlo transport, and reaction-specific secondary-field monitoring. Boron neutron capture therapy (BNCT) is treated as the clinically mature benchmark for RER, whereas PBCT and the emerging15N(p,α)12C-based proton-carbon-alpha therapy (Proton-CAT) are discussed as early-stage comparative cases. BNCT has the strongest clinical evidence base, while PBCT remains constrained by narrow resonances, low reaction yield, and limited observability. Proton-CAT provides a candidate 4.439 MeV prompt-γobservable whose verification value still requires detector-level validation and delivery/selectivity assessment. Overall, clinically credible RER requires coordinated progress in uncertainty-aware nuclear data, detector-integrated transport modeling, isotope delivery and tumor-to-normal biodistribution assessment, and high-sensitivity radiation monitoring for therapy verification.
7 July 2026
Read appraisal →Journal of medical systems
Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation
Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharing patient data. This approach has been applied to complex tasks such as medical image-to-image (I2I) translation, including MRI-to-synthetic CT (sCT) generation. However, existing federated I2I frameworks often assume privacy preservation as an inherent property of FL rather than a requirement to be explicitly validated, leaving their robustness to representative adversarial threat scenarios largely unexplored. In this study, we evaluated the vulnerability of a federated MRI-to-sCT translation framework (FedSynthCT-Brain) to three representative attack classes: Deep Leakage from Gradients (DLG), Federated Membership Inference Attack (FedMIA), and data poisoning. The efficacy of corresponding defense mechanisms, such as Secure Aggregation (SecAgg) and Byzantine-robust median aggregation (FedMedian), were assessed. DLG enabled only the recovery of coarse anatomical structures, with no clinically identifiable details (SSIM ≤ 0.16, PSNR ≤ 11 dB) across clients, suggesting limited vulnerability under the evaluated DLG setting. In contrast, FedMIA achieved high membership discrimination, with AUC scores between 0.92 and 0.99, revealing a critical privacy vulnerability. The introduction of SecAgg reduced AUC values to near-random levels (0.23-0.56) across all centers without impacting synthesis quality. Under high-noise poisoning, the standard federated averaging (FedAvg) aggregation rendered the federation inoperative, while FedMedian restored performance close to the no-poisoning baseline in most scenarios, with significant residual degradation in specific center configurations. At low noise levels, the advantage of FedMedian was less consistent, as low-level noise injection may be indistinguishable from natural heterogeneity across centers, potentially enabling stealthy degradation. These findings demonstrate that federated I2I translation frameworks are not inherently secure and require explicit, multi-layered evaluation. As FL is increasingly adopted in clinical workflows, our results underscore the necessity of integrating cryptographic, algorithmic, and infrastructural safeguards for secure deployment.
6 July 2026
Read appraisal →Journal of hematology & oncology
Emerging artificial intelligence advances in oncology: latest updates from the 2026 AACR annual meeting.
Advances presented at the 2026 American Association for Cancer Research (AACR) Annual Meeting highlight a shift from standalone artificial intelligence (AI) models to integrated, agentic systems across oncology. Platforms such as Synapse enable large-scale data coordination, supporting interoperable and reproducible research. Building on this foundation, conversational and multi-agent AI tools (e.g., DrBioRight, GP CoPilot, Isabl AI Agent) allow natural language interaction with multimodal cancer data, lowering technical barriers. Agentic frameworks for real-world data (RWD) transformation, including clinical document abstraction, cohort extraction, and social determinants of health (SDOH) analysis, demonstrate high accuracy and scalability, while self-critical systems improve reliability. Clinically, AI shows growing impact through validated imaging biomarkers, enhanced trial matching, and scalable cohort identification. In parallel, multi-agent systems are accelerating therapeutic discovery, including CAR-T development and immunotherapy target identification. Collectively, these advances position AI as an active, collaborative partner in cancer research and precision oncology.
5 July 2026
Read appraisal →Medical physics
Gamma Knife treatment planning using knowledge-based reinforcement learning
BACKGROUND: Inverse planning is often used for Gamma Knife radiosurgery, allowing clinicians to mathematically specify desired clinical objectives and dose limits. The objectives are controlled by weights that are manually tuned to find the desired trade-off, which varies from case to case. Automation of this process can reduce clinical workload and improve consistency in plan quality. PURPOSE: To train a deep reinforcement learning agent using a reward function that incorporates the clinical metrics from past plans into its scoring criteria. The metric trade-off from the clinical plan is scored higher than all others, guiding the agent to produce plans with similar trade-offs. METHODS: An agent was trained to adjust the two priority weights (i.e., digital slider bars) in the clinical inverse planner. The agent consists of a neural network that receives the metrics and dose distribution of the current plan and the target and organ-at-risk masks as inputs. These methods were demonstrated on a dataset of 204 single-target metastases and a dataset of 71 acoustic neuroma cases. The cases were split into training, validation, and testing sets of size 123/41/40 and 42/14/15 for the metastases and acoustic neuromas, respectively. RESULTS: On the metastases test dataset, the agent achieved a significantly higher (p = 0.0136) average plan score (3.925 ± 0.130) compared to the default slider plans (3.874 ± 0.147). On the acoustic neuromas test dataset, the agent achieved a higher (p = 0.4493) average plan score (4.035 ± 0.177) compared to the default slider plans (3.995 ± 0.365). The higher plan scores are reflected in the four plan quality metrics: the agent's plans, on average, had metrics more similar to the clinical plans, compared to the default slider plans, for both test datasets. CONCLUSIONS: The proposed reward function enabled the agent to learn to find plans that aligned with historical planning decisions. Future work will investigate providing the agent with additional inputs that can explain the variability in planning decisions, which would further improve its performance.
4 July 2026
Read appraisal →BMJ health & care informatics
Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.
OBJECTIVE: This study developed and validated monolingual and bilingual sentence-bidirectional encoder representations from transformers (SBERT) models for detecting cancer recurrence within Thai-English electronic medical records (EMRs) from Thai cancer hospitals. METHOD: A multicentre dataset of 32 436 documents from 1250 patients was used for model development. External validation involved an independent dataset of 9244 documents from 384 patients across two Thai cancer hospitals. Performance was benchmarked against a fine-tuned PubMedBERT (MetBERT). RESULTS: The development dataset included breast (43.9%), colorectal (12.1%), cervical (28.0%) and head and neck (16.0%) cancers. MetBERT achieved the highest area under the precision-recall curve (AUPRC) for locoregional versus no recurrence (11.1%) and locoregional versus distant recurrence (91.7%), while monolingual-SBERT excelled at distant versus no recurrence (32.0%). External validation demonstrated MetBERT superiority for locoregional versus no recurrence (9.30%-21.50%). For distant versus no recurrence, bilingual-SBERT performed best with AUPRC 17.55%-24.39%. While MetBERT led in distinguishing locoregional versus distant recurrence (88.30%-94.70%), bilingual-SBERT demonstrated robust external validation performance (AUPRC 85.25%-91.80%). DISCUSSION: Low AUPRC values (9%-32%) reflect the extreme class imbalance in real-world data (~1% recurrence prevalence). Despite this, fine-tuned MetBERT achieved highest performance, while bilingual-SBERT demonstrated superior robustness during external validation. This validates sentence embedding models for handling mixed Thai-English medical records in multilingual clinical environments. CONCLUSION: Sentence embedding frameworks provide a practical, generalisable solution for detecting cancer recurrence within multilingual EMRs. Despite text-length constraints, these models are suitable for clinical integration as a screening tool for cancer registry workflows.
4 July 2026
Read appraisal →Physics in medicine and biology
Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators
Objective.This study aimed to develop a dose prediction model for hybrid applicators in cervical cancer brachytherapy.Approach.A total of 216 previous treatment plans were collected and divided into three datasets: training (161 plans), testing (40 plans), and clinical evaluation (15 plans). The treatment plans included three hybrid applicator types: tandem and ovoids combined with interstitial needles, tandem and ring combined with interstitial needles, and tandem and cylinder combined with interstitial needles. The model was validated on the testing dataset using mean absolute error (MAE) for voxel-wise dose evaluation and on the clinical evaluation dataset using dice similarity coefficient (DSC) and dose statistic differences.Main results.The model achieved a voxel-wise MAE of 0.45 ± 0.27 Gy and showed no statistically significant differences (p> 0.05) in all key dose-volume histogram parameters when compared to clinical plans. The DSC values ranged from 0.74 to 0.77. Small dosimetric differences between the optimized and manually generated plans were observed for the high-risk clinical target volume (HR-CTV) D98%, HR-CTV D90%, and bladder D2cc.Significance.The developed dose prediction model demonstrates significant potential for application in hybrid applicator brachytherapy. This model can provide guidance for dose delivery during dwell time optimization. As a reference for optimization, it has the potential to streamline the planning process, reduce planning time, and improve consistency in adaptive workflows.
3 July 2026
Read appraisal →Physics in medicine and biology
Deep learning based time series analysis for breathing phase prediction in phase-gated proton therapy
Objective. Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery.Approach. Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use.Main Results. The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels.Significance. DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.
2 July 2026
Read appraisal →Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Advancing radiotherapy with deep learning: A review of dose prediction models
BACKGROUND: Artificial intelligence (AI), especially deep learning algorithms, holds significant promise in transforming radiation therapy by enabling more precise, personalized, and efficient treatment planning. In particular, deep learning models have demonstrated their utility in critical tasks such as dose prediction, helping to streamline complex processes and improve patient outcomes. METHODS: This paper provides a comprehensive review of recent research on deep learning applications for radiotherapy dose prediction, highlighting the architectures, datasets, and methodologies used. RESULTS: The review highlights the advancements in accuracy, efficiency, and robustness achieved by deep learning-based dose prediction models. These approaches help reduce treatment planning time and minimize human error, thereby improving clinical workflows. CONCLUSION: Deep learning offers promising solutions to enhance radiotherapy dose delivery. Future research should focus on developing the generalized dose prediction models, multi-institutional validation frameworks, explainable AI approaches for clinical trust, real-time adaptation capabilities, uncertainty quantification methods, and strategies for handling rare anatomical variations.
1 July 2026
Read appraisal →Biomaterials
Artificial intelligence in biomaterials for oral oncology
Oral cancer and oral potentially malignant disorders (OPMDs) remain a significant challenge in diagnosis and therapy, primarily due to inherent limitations in early detection, targeted treatment, and postoperative rehabilitation. Conventional diagnostic and therapeutic modalities often lack sufficient sensitivity, specificity, and effectiveness in restoring oral function. Biomaterials including nanoparticles, hydrogels, and scaffolds, offer versatile solutions by virtue of their tuneable properties, biocompatibility, and versatility in drug delivery and tissue engineering. However, their clinical translation is limited by the need for personalisation and lingering efficacy concerns. Artificial intelligence (AI) has emerged as a transformative approach to advance the design, optimisation, and application of biomaterials in oral oncology. By integrating machine learning (ML) and data-driven modelling, AI enhances diagnostic accuracy through biosensing and radiomic analysis, guides the rational design of drug carriers and dosing regimens, and facilitates computer-aided scaffold fabrication for maxillofacial reconstruction. This review summarises recent advances at the intersection of AI and biomaterials in the context of oral cancer and OPMDs, highlighting innovations in early detection, targeted therapy, and postoperative repair. It also discusses current barriers, including data quality, model generalizability, and regulatory oversight, and outlines future directions for interdisciplinary research. When properly integrated, AI-enabled biomaterials hold considerable potential to deliver more precise, efficient, and patient-tailored solutions for oral cancer management.
1 July 2026
Read appraisal →The British journal of radiology
Artificial intelligence in nuclear medicine
Artificial intelligence (AI) holds great promise for advancing diagnostics and treatment in nuclear medicine. The rapid growth of AI over the past decade has been largely driven by advances in hardware components such as graphics processing units (GPUs) and the introduction of deep learning (DL) and convolutional neural networks (CNN). The integration of AI and medical imaging has the potential to revolutionize nuclear medicine by, for example, accelerating image acquisition, enhancing image quality, enabling advanced image generation, assisting image interpretation, and aiding treatment planning. Clinical applications have been demonstrated for most medical specialties, including oncology, neurology, and radionuclide therapy. The utilization of AI to provide automated, standardized procedures can help bring advanced imaging from major university centres to smaller local clinics, thus benefiting a broader range of patients. Additionally, AI has vast potential for predicting optimal treatment strategies, assessing risk, optimizing patient flow and outcomes, and even improving productivity, but these capabilities have yet to be fully utilized. The fraction of clinical AI applications in general healthcare reaching beyond the prototyping phase is reported to be as low as 2%. Indeed, in nuclear medicine, very few AI developments have reached commercial maturity. Currently, most AI applications in nuclear medicine follow the imaging flow from image acquisition and reconstruction, post-processing and image preparation, image analysis, and decision support for clinical interpretation. Below, we will briefly review selected areas and comment on challenges and opportunities for AI in nuclear medicine, with a special focus on the transition from development to clinical implementation.
1 July 2026
Read appraisal →Physics in medicine and biology
Domain-specific adaptation for MR image synthesis with text-guided diffusion
Objective.Deep learning in medical imaging is severely constrained by data scarcity. Data synthesis offers a promising solution, but existing generative models have difficulty in restoring pathological texture features when trained on small-scale datasets. To address this, we propose a domain-specific, partition-based parallel text-guided latent diffusion model (LDM) for medical image synthesis.Approach.Each LDM operates on a defined image domain and is fine-tuned to reproduce specific texture characteristics. Diseased regions are identified from segmentation masks, while healthy regions are further subdivided using Voronoi-grayscale adaptation, enabling localized texture preservation. The fine-tuned LDMs independently synthesize corresponding image partitions, which are subsequently merged and denoised to form complete synthetic images with paired segmentation masks.Main results.We evaluated the approach on glioma MRI data, achieving a Fréchet Inception Distance of 13.65, demonstrating high perceptual realism. Texture fidelity was further supported by SSIM of 0.9674 and radiomic feature distribution analysis, both confirming close alignment between real and synthetic images. In a blinded visual Turing test, three radiologists achieved an average sensitivity of only 25.5% when identifying synthetic MRI slices, resulting in a 74.5% deception rate, and 41% of the synthetic samples were universally misclassified as real by all experts. In downstream experiments, U-Net trained on the synthetic-augmented dataset improved DSC by 14% on average.Significance.These results demonstrate that the proposed domain-specific adaptation framework can generate perceptually plausible, structure-preserving synthetic MRI slices in data-constrained environments, while improving downstream segmentation performance. The method therefore shows potential as an augmentation-oriented tool for AI model development, clinical teaching, assisted diagnosis, and rare-disease research.
25 June 2026
Read appraisal →International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
The role of capacitive hyperthermia as an adjunct treatment in oncology: a systematic review of randomized phase III trials.
INTRODUCTION: Capacitive hyperthermia (cHT) has been established for superficial tumors but the efficacy of cHT for intermediate or deep-seated tumors remains a subject of debate. This review is an evaluation of the current clinical evidence on the effectiveness of cHT in the treatment of tumors that are located deeper within the body. METHODS: A systematic review was conducted in accordance with the PRISMA guidelines of Phase III randomized controlled trials that included cancer patients of all ages and sexes with confirmed malignant tumors undergoing radiotherapy (RT) and/or chemotherapy (CT), with or without additional cHT. The literature search was conducted in PubMed and the Cochrane Register of Controlled Trials (CENTRAL). The primary endpoints included overall survival (OS), tumor response (TR; encompassing complete response (CR)), local control (LC), and cHT treatment-related toxicity (TRT). RESULTS: A total of nine randomized phase III trials were included in the study. The combination of cHT with RT and/or CT was found to improve both LC and OS in three out of five trials conducted in patients with cervical cancer. The analysis of both head and neck cancer trials demonstrated a marked increase in TR rates in cases where cHT was employed. Conversely, no such benefit was observed in non-small cell lung cancers and in a large multicenter cervical cancer trial. The treatment was well tolerated, with mild to moderate adverse events being the most common. CONCLUSION: It has been demonstrated that the combination of cHT with either RT or CT may improve TR and LC and, in certain cases, OS when compared with RT with or without CT alone. The occurrence of significant side effects is rare.
24 June 2026
Read appraisal →Journal of neuro-oncology
AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.
BACKGROUND: Glioblastoma (GBM) remains one of the most lethal adult primary brain tumors, and neurosurgical decision-making increasingly depends on integrating imaging, molecular, perioperative, and post-treatment data. Artificial intelligence (AI) methods have been proposed for several clinically relevant GBM tasks, but the literature remains heterogeneous and difficult to translate into practice. METHODS: We performed a PROSPERO-registered systematic review of AI, machine learning, and deep learning studies using MRI-derived and/or multimodal perioperative data in GBM for prognosis, risk stratification, treatment-response assessment, post-treatment classification, recurrence/progression prediction, and molecular prediction. Risk of bias was assessed using PROBAST-informed criteria. RESULTS: Thirty studies were included. Survival-focused tasks predominated (20/30, 66.7%), with radiomics plus conventional machine learning as the most common model family (13/30, 43.3%), followed by deep learning (8/30, 26.7%) and hybrid deep learning plus radiomics approaches (4/30, 13.3%). Validation was predominantly internal, and external validation was uncommon (5/30, 16.7%). CONCLUSIONS: AI shows promise for prognosis and treatment stratification in GBM neurosurgery, but current evidence is limited by heterogeneity, incomplete external validation, and inconsistent methodological reporting. CLINICAL TRIAL NUMBER: Not applicable.
20 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Photobiomodulation for oral mucositis in cancer care: prevention, clinical management, and evidence from randomized controlled trials.
PURPOSE: This systematic review aimed to evaluate the efficacy of photobiomodulation (PBM) for the prevention and clinical management of oral mucositis (OM) in adult cancer patients, with particular emphasis on protocol characteristics potentially associated with favorable clinical outcomes. METHODS: A systematic search of PubMed, Web of Science, and Scopus was conducted up to March 31, 2026, in accordance with PRISMA guidelines. Randomized controlled trials in adult cancer patients comparing PBM with sham interventions were included. Outcomes of interest included OM incidence, severity, pain, and related clinical outcomes. The review protocol was prospectively registered in PROSPERO. RESULTS: Fifteen randomized controlled trials involving 868 adult patients were included. Most studies reported favorable effects of PBM, including reductions in severe OM incidence, pain intensity, analgesic requirements, and nutritional support needs compared with controls. Preventive intraoral PBM protocols were generally associated with more favorable outcomes, although variability in treatment parameters, delivery methods, and outcome reporting limited comparability. Most included studies demonstrated acceptable methodological quality. CONCLUSIONS: PBM appears to be a promising supportive care intervention for reducing the burden of OM in adult cancer patients, particularly in preventive settings. However, variability across protocols precludes definitive conclusions regarding optimal treatment parameters. Further high-quality randomized trials with standardized reporting are needed to refine protocol recommendations and expand evidence in therapeutic settings.
20 June 2026
Read appraisal →Signal transduction and targeted therapy
Liposomal doxorubicin plus nab-paclitaxel with/without chemoradiotherapy in head and neck adenoid cystic carcinoma: single-arm phase II study
Given no standard therapies, this single-arm, open-label, multicenter phase II study was conducted in unresectable locally advanced or recurrent/metastatic adenoid cystic carcinoma of the head and neck (LA or R/M ACCHN). Patients received chemotherapeutic nanomedicines (20 mg/m2 liposomal doxorubicin plus 120 mg/m2 nab-paclitaxel on days 1 and 8) for three 21-day cycles. Subsequently, patients eligible for radiotherapy received concurrent chemoradiotherapy (cCRT; helical tomotherapy or intensity-modulated radiotherapy and nab-paclitaxel), whereas those ineligible for radiotherapy continued 2-3 cycles of original chemotherapy. Between May 27, 2021, and November 13, 2024, 31 enrolled patients were evaluable for efficacy and safety. The primary endpoint objective response rate (ORR) was 90.3% (95% CI, 74.2-98.0%), with 15 (48.4%) complete responses and 13 (41.9%) partial responses, meeting the prespecified primary endpoint. The median duration of response was 18.5 (95% CI, 0-45.8) months. With a median follow-up of 15.7 months by data cutoff (June 25, 2025), the median progression-free survival (PFS) was 25.7 (95% CI, 6.5-44.8) months; the 1-year PFS was 77.1%. The median overall survival (OS) was not reached (NR), with a 1-year OS of 90.0%. Patients who received cCRT had a prolonged PFS (NR vs. 12.8 months) and OS (NR vs. 21.2 months) compared to those who did not. Grade 3 treatment-related adverse events (TRAEs) occurred in 8 (25.8%) patients. No ≥grade 4 TRAEs or treatment-related deaths occurred. This study demonstrated tumor responses among the highest reported to date, with a manageable safety profile in unresectable LA or R/M ACCHN. Chemotherapeutic nanomedicines, especially sequential cCRT for appropriate patients, may represent an effective and safe option.
14 June 2026
Read appraisal →Journal of neuro-oncology
Symptomatic radiation necrosis following intracranial brachytherapy: a systematic review and meta-analysis
PURPOSE: To estimate the pooled incidence of symptomatic radiation necrosis (RN) following intracranial brachytherapy and explore associations between clinical and technical variables and RN risk. METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched from 1954 - 2024 in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible human studies reported symptomatic RN after intracranial brachytherapy. RN ascertainment was secondarily classified as clear, partial, or unclear based on reproducibility of diagnostic criteria. A random-effects generalized linear mixed model was used to pool incidence rates, with subgroup analyses by tumor type, implant technique, isotope, dose rate, and prior radiation. Study quality was assessed using National Institutes of Health tools. RESULTS: Eighty-three studies encompassing 3,666 patients were included. The pooled incidence of symptomatic RN was 5.67% (95% CI: 3.84%-8.29%; I2 = 75.7%). RN ascertainment was clear in 25 studies, partial in 43, and unclear in 15. In sensitivity analyses, pooled symptomatic RN incidence was 8.71% among studies with clear RN definitions and 7.48% among studies with clear or partial definitions. Subgroup RN rates were 3.72% for low-grade gliomas, 8.44% for high-grade gliomas, 2.07% for brain metastases, and 8.98% for meningiomas. Isotope-specific rates were 5.83% for Iodine-125, 7.61% for Iridium-192, and 2.07% for Cesium-131. No statistically significant subgroup differences were observed. CONCLUSIONS: Symptomatic RN occurs in approximately 5.7% of patients following intracranial brachytherapy, within the range reported for other focal radiation therapies. Subgroup findings were not statistically significant and should be interpreted in the context of clinical and methodological heterogeneity.
13 June 2026
Read appraisal →The Cochrane database of systematic reviews
Bone marrow-sparing radiotherapy in women with cervical cancer treated with radiochemotherapy
This is a protocol for a Cochrane Review (intervention). The objectives are as follows: Primary objective To evaluate the benefits and harms of bone marrow-sparing radiotherapy compared with non-bone marrow-sparing radiotherapy in women with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Secondary objectives To investigate whether effects differ according to clinical, histopathological, and technical characteristics, focusing on: acute haematologic toxicity; acute gastrointestinal and genitourinary toxicity; and bone marrow dose-volume parameters associated with acute haematologic toxicity, including exploration of dose-volume ranges relevant for clinical implementation.
13 June 2026
Read appraisal →Journal of neuro-oncology
Laser interstitial thermal therapy (LITT) for brain metastases: a systematic review and pooled analysis of Efficacy/Safety and the role of combination treatments
PURPOSE: Laser interstitial thermal therapy (LITT) has been used for brain metastases (BM), particularly for recurrent lesions and radiation necrosis after stereotactic radiosurgery (SRS). As SRS is increasingly used, differentiating tumor recurrence from radiation necrosis has become a frequent clinical problem. Recently, LITT has also been combined with other treatments to improve outcomes. However, prior reviews have primarily focused on LITT monotherapy and have provided limited evaluation of emerging multimodal strategies. We aim to summarize the current literature on LITT for BM, with particular attention to local control, safety, factors associated with outcomes, and the potential role of combination treatment strategies. METHODS: PubMed, Scopus, and Web of Science were searched following PRISMA guidelines. We included studies reporting local control outcomes following LITT for BM or radiation necrosis secondary to previously treated BM. We used a combination of the following terms: ("laser interstitial thermal therapy" OR "LITT" OR "laser ablation") AND ("brain" OR "cerebral" OR "intracranial") AND ("metastases" OR "metastasis"). Graphical data were extracted using WebPlotDigitizer. A proportional meta-analysis was conducted to estimate pooled LTC rates at 6 and 12 months following treatment. RESULTS: A total of 254 studies were identified after duplicate removal, of which 30 met the inclusion criteria. These included 26 studies on LITT monotherapy and 4 on combination therapies, comprising 972 patients. Median age ranged from 51 to 69 years and median tumor volume from 2.2 to 8.5 cm. SRS was the most common prior treatment. Among patients treated with LITT monotherapy, pooled 6- and 12-month LTC rates were 74.4% and 67.9%, respectively. In subgroup analyses, corresponding rates were 74.0% and 67.9% for brain tumor metastases, and 76.6% and 65.7% for radiation necrosis. Studies of combination therapy reported local control rates of 76% for LITT+SRT (stereotactic radiotherapy), 94.7% for LITT+ICB (immune checkpoint blockade), and 100% at 12 months in both studies for LITT+cSRS (consolidation SRS). Transient neurological deficits were the most common adverse events, whereas permanent deficits were less frequent and procedure-related complications were reported in 20 patients. Significant predictive factors for better local control included greater extent of ablation, smaller pre-treatment tumor volume, pathology consistent with radiation necrosis and post-LITT chemotherapy. CONCLUSIONS: Current evidence suggests that LITT may provide favorable local control with an acceptable safety profile in selected patients, and combination approaches may further improve outcomes. However, the available evidence remains largely retrospective and heterogeneous, and further prospective studies are needed to better define the role of LITT and combination treatment strategies.
12 June 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Neoadjuvant Taxane Plus Trastuzumab and Pertuzumab With or Without Carboplatin in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: The Randomized Noninferiority Phase III neoCARHP Trial
PURPOSE: The neoCARHP aimed to investigate the efficacy and safety of investigator-selected taxane (docetaxel, paclitaxel, or nab-paclitaxel) plus trastuzumab and pertuzumab, with carboplatin (TCbHP) or without carboplatin (THP), in stage II and III human epidermal growth factor receptor 2 (HER2)-positive breast cancer. METHODS: The neoCARHP was a multicenter, randomized, phase III, noninferiority study. Eligible patients were women age 18 years or older with previously untreated, stage II and III, HER2-positive invasive breast cancer. Patients were randomly assigned (1:1) to receive six 3-week cycles of TCbHP or THP. The primary end point was pathologic complete response (pCR) rate in the breast and axilla (ypT0/is ypN0) in the modified intention-to-treat (mITT) population (all randomly assigned patients receiving at least one dose of study treatment). Safety was evaluated in all patients who received any study treatment. RESULTS: Between April 30, 2021, and August 27, 2024, 774 patients were randomly assigned and 766 were included in the mITT population (382 in THP and 384 in TCbHP). pCR was achieved in 245 (64.1% [95% CI, 59.1 to 69.0]) patients in the THP group and 253 (65.9% [60.9-70.6]) in the TCbHP group (absolute difference, -1.8% [95% CI, -8.5 to 5.0]; odds ratio, 0.93 [95% CI, 0.69 to 1.25]; Pnoninferiority = .0089). The THP group had fewer grade 3 and 4 adverse events (20.7% v 34.6%) and serious adverse events (1.3% v 4.7%) than the TCbHP group. The most common grade 3 and 4 adverse events with THP were neutropenia (6.8% v 16.4% with TCbHP), leukopenia (5.5% v 14.8%), and diarrhea (2.6% v 4.2%). No treatment-associated deaths occurred. CONCLUSION: THP provided noninferior pCR rates and improved tolerability compared with TCbHP. Omitting carboplatin may be applicable in HER2-positive breast cancer.
12 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Developing the multidisciplinary and multispecialty workforce and the systems needed to sustain high-quality care from diagnosis through long-term survivorship
With the growing population of individuals living with and beyond cancer-who often have a variety of complex health needs across their lifetimes-coordinated multidisciplinary, multispecialty expert care is needed to ensure comprehensive survivorship cancer care beginning at diagnosis. Guided by the July 2023 US National Academies of Sciences, Engineering, and Medicine workshop, we describe the evidence and propose steps to (1) identify and promote clinician competencies through education and training; (2) improve communication and care coordination, including leveraging the electronic health record and digital health innovations; (3) examine health system interventions and financial strategies to facilitate multidisciplinary, multispecialty survivorship care; and (4) identify policy, payment, and advocacy challenges and opportunities to achieve equitable, accessible, high-quality care. Although the recommendations are centered on the United States, their applicability to other countries should also be explored.
1 June 2026
Read appraisal →Radiation oncology (London, England)
HyperSightTM-CBCT based monitoring of sarcopenia during definitive radiotherapy of prostate cancer: a longitudinal feasibility study
BACKGROUND: Sarcopenia and treatment-related muscle loss are associated with increased toxicity and reduced resilience in cancer patients. HyperSight™ cone-beam CT (CBCT) provides improved soft-tissue contrast compared with conventional CBCT and may enable reliable longitudinal skeletal-muscle monitoring during image-guided radiotherapy (IGRT). This study evaluated the feasibility of HyperSight™ CBCT-based muscle assessment in prostate cancer patients and explored the clinical relevance of intratreatment muscle decline. METHODS: Eighty-eight patients with non-metastatic prostate cancer were treated on an Ethos® system with HyperSight™ CBCT. Patients received ultrahypofractionated (UHF) or normo-/moderately hypofractionated (NF/MHF) radiotherapy; 28% received androgen-deprivation therapy. Cross-sectional muscle area (CSA) and radiodensity (HU) were assessed on planning CT (PCT) and serial CBCTs (first, second, last fraction [FF, SF, LF]); HU values were z-standardized to subcutaneous fat (zHU). Agreement between PCT and CBCT was evaluated using concordance correlation coefficients (CCC), regression, and Bland-Altman analysis. Longitudinal changes were analyzed using mixed-effects models, and associations with acute genitourinary/gastrointestinal (GU/GI) toxicity (RTOG ≥ 2) using regression models. RESULTS: HyperSight™ CBCT showed excellent agreement with PCT for CSA (CCC = 0.981). The NF/MHF cohort showed a significant longitudinal CSA decline (FF-LF: -153.0 mm², p = 0.021), whereas no significant within-cohort change was observed in UHF. Muscle density (zHU) decreased significantly in both cohorts (NF/MHF: p < 0.001; UHF: p = 0.0017). Weight loss and age were independently associated with zHU change, while no independent predictors of CSA decline were identified. In the NF/MHF cohort, CSA loss > 5% was associated with acute GU toxicity in univariate analysis (OR 4.90, p = 0.033; Fisher's exact p = 0.014), but not after adjustment for age, weight loss, ECOG performance status, and ADT; no associations were found for GI toxicity or in the UHF cohort. CONCLUSIONS: HyperSight™ CBCT enables quantitative skeletal-muscle assessment during prostate radiotherapy and detects intratreatment reductions in muscle density, which may represent an early imaging marker of muscle decline. Muscle loss showed an exploratory association with acute GU toxicity in NF/MHF patients in univariate analysis, but not after adjustment. These findings support the feasibility of CBCT-based muscle monitoring and warrant further evaluation in larger cohorts. CLINICAL TRIAL NUMBER: Not applicable.
1 June 2026
Read appraisal →European radiology experimental
Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.
OBJECTIVE: Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hinder adoption. This study, led by the European Society of Medical Imaging Informatics (EuSoMII) in collaboration with the European Federation of Radiographer Societies (EFRS), aimed to create an accessible, centralised, searchable database including all AI courses in Europe. MATERIALS AND METHODS: An electronic survey was developed to collect data on European AI course characteristics, such as format, delivery, content, target audience and European Qualifications Framework (EQF) level. This was disseminated via purposive sampling through social media and mailing lists of the EuSoMII and the EFRS between September 2024 and January 2025. Quantitative data were analysed using descriptive statistics and visual representations using Python Seaborn and Geopandas. RESULTS: This study identified 29 AI courses in Europe. Of them, 53.6% were offered by universities. Courses targeted radiographers (59%), medical physicists (52%), and radiologists (41%), mainly at EQF level 7 (44.4%). Most courses were standalone (65.6%) and online (55.1%), while 41.3% were free of charge. English was the primary language of delivery (79%). CONCLUSIONS: Different AI courses across Europe offer some entry-level knowledge but are often short in duration. Expanding formats, building practical competencies, providing multilingual access, and European-wide reach are essential for meaningful, practical, and equitable AI integration. RELEVANCE STATEMENT: With the scaling-up of AI adoption in medical imaging and radiation oncology, there is a variety of AI education provisions currently available. Accessing these options via an open, centralised, regularly updated database enables people to make an informed decision about their training and practise safely and meaningfully. KEY POINTS: We identified 29 different AI European courses varying in language, content, and delivery. Many clinical practitioners and researchers are unaware of these resources. We need a centralised database for customising AI learning choices and guiding future course design.
31 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Concurrent de novo glioblastoma and meningiomatosis: a case report and systematic review of clinical, molecular, and topographical characteristics
INTRODUCTION: The co-occurrence of glioblastoma (GBM) and meningioma in a single patient is an exceptionally rare clinical phenomenon, often associated with prior irradiation or genetic syndromes. This study presents a unique case of concurrent de novo GBM and meningiomatosis and provides a systematic review of the literature to characterize this rare association. CASE PRESENTATION AND METHODS: A 78-year-old female with a known history of untreated meningiomatosis presented with refractory seizures and aphasia. Imaging revealed multiple stable extra-axial lesions and a new, distinct intra-axial right temporal lesion. The patient underwent a temporal lobectomy with gross total resection. Histopathology confirmed WHO grade 4 GBM (IDH-wildtype). Following multidisciplinary review, she completed adjuvant radiotherapy. At 6-month follow-up, she remained clinically stable with no recurrence. A systematic review was conducted, identifying 34 cases of concurrent GBM and meningioma. Data regarding demographics, topography, molecular profiles, and survival were analyzed. RESULTS: The median age was 63 years (range 30-86), with a slight male predominance (52.9%). Tumors were synchronous in 88.2% of cases. Topographically, 47.1% were "collision" or adjacent tumors, while 32.3% occurred in different hemispheres. Most meningiomas were WHO grade I (79.4%), while 97.1% of glial tumors were grade 4 GBM. Molecular analysis revealed classic independent drivers: 22q loss/NF2 mutations in meningiomas and 10q loss/EGFR amplification in GBMs. Median overall survival was 7 months (range 0.5-24 months), with modern trimodal therapy (Stupp protocol) showing a trend toward extended survival compared to older cohorts (Fig. 1). CONCLUSION: The coexistence of GBM and meningioma appears mostly coincidental, driven by distinct molecular pathways rather than a common progenitor. Despite the benign nature of the associated meningioma, prognosis is dictated by the aggressive GBM component. Early surgical intervention and modern adjuvant therapy remain essential for optimizing survival in these complex cases.
31 May 2026
Read appraisal →Journal of neuro-oncology
Intramedullary or invasive characteristics of spinal melanocytomas: a PRISMA compliant systematic review and pooled case analysis
PURPOSE: Primary meningeal melanocytomas (MMs) of the spinal cord are rare lesions. Even rarer are intramedullary and invasive variants. These intramedullary lesions may exhibit more aggressive behavior than their "benign" classification appreciates. The role of adjuvant radiotherapy (aRT) in this context remains uncertain. METHODS: This study is a PRISMA-compliant systematic review with a pooled-case analysis, including two recent institutional cases included as illustrative examples. RESULTS: Sixty-three reports were identified of primary intramedullary or invasive MMs. Among 56 patients with available recurrence data, 8 of 30 patients (26.7%) experienced recurrence after gross-total resection (GTR) and in 8 of 22 (36.4%) after near-total or subtotal resection (NTR/STR), with no significant difference between groups (two-sided Fisher's exact, P = 0.548). No recurrence was observed among three patients who received aRT upfront post GTR, compared with 8 of 27 (29.6%) who did not receive radiation (two-sided Fisher's exact, P = 0.545); however, this subgroup was markedly underpowered and should be interpreted as hypothesis-generating only. Nine patients developed metastatic disease or progression, including one post biopsy. Of the eight who underwent resection at index surgery, 2 (25%) had undergone GTR and 6 (75%) STR. CONCLUSIONS: These findings suggest that the designation of MMs as benign lesions may underestimate the oncologic potential of intramedullary or invasive disease. Consideration of aRT on an individualized basis for patients with intramedullary or invasive disease is reasonable, regardless of extent of resection.
30 May 2026
Read appraisal →Biomedical physics & engineering express
RADIANT: A fully configurable radiotherapy dose prediction framework
Objective.Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Recently, deep learning has arisen as a powerful method for predicting patient-specific dose distributions. This work presents the radiotherapy dose inference and analysis toolkit (RADIANT), an open-source, fully configurable framework for 3D radiotherapy dose prediction. Built upon the Medical Imaging Segmentation Toolkit, RADIANT supports diverse network architectures, loss functions, and training strategies.Approach.We demonstrate its capabilities on cervical and prostate treatment plans generated with the radiation planning assistant, and on head and neck cancer plans from the American Association of Physicists in Medicine OpenKBP challenge data. For cervical cancer, we trained and compared nnU-Net, FMG-Net, W-Net, ddU-Net, and Swin UNETR using clinical metrics such as dose score, homogeneity index, and percent errors inD95,D98, andD99. Benchmarking was also performed on the OpenKBP dataset using dose score and dose-volume histogram (DVH) score for comparison with top challenge submissions.Results.Our results indicate that RADIANT provides a scalable platform for the rapid development and benchmarking of dose prediction models for selected cancer sites (cervix, prostate, head and neck). For cervical cancer, the best configuration (nnU-Net architecture, mean absolute error loss, cosine learning rate scheduler) achieved a dose score of 1.31 and sub-1%errors inD95andD98on the training set, and a dose score of 1.20 on the test set. The same configuration performed best on prostate data with a dose score of 1.96 on a test set. On the head and neck OpenKBP data, RADIANT achieved a dose score of 2.702 and a DVH score of 1.495, competitive with top challenge results.Significance.This work provides an open-source, fully configurable framework for deep learning-based dose prediction. RADIANT facilitates reproducible experimentation across data preprocessing, model configuration, training, and evaluation for multiple cancer sites and anatomical structures.
30 May 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Artificial intelligence (AI) is reshaping cancer research and clinical oncology by enabling large-scale analysis of complex biomedical data. Although early AI efforts focused on single-modality tasks such as imaging interpretation or pathology classification, contemporary oncology increasingly requires integrated reasoning across heterogeneous inputs, including imaging, digital pathology, and genomics. Multimodal artificial intelligence (MMAI) models represent a major evolution by fusing these data types into unified predictive systems capable of informing diagnosis, risk stratification, treatment selection, and disease monitoring. This manuscript reviews the state of AI in oncology and highlights emerging foundation models within the field. There will be a focus on histopathology, and how this can be incorporated into risk assessment models. There will also be examples of MMAI utility in radiology and spatial proteomics. Additionally, we will discuss ethical and legal implications of MMAI in modern oncology, and recommendations for responsible application within clinical oncology practice.
30 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patients' experiences of "time toxicity": a qualitative meta-synthesis
OBJECTIVE: To systematically review and synthesize qualitative studies on cancer patients' experiences of time toxicity, providing evidence to inform policy making. METHODS: We searched PubMed, Web of Science, Cochrane Library, Embase, CINAHL, CNKI, Wanfang, VIP, SinoMed, and other databases for qualitative studies on cancer patients' experiences of time toxicity, from database inception to November 2025. Included studies were appraised using the 2016 edition of the JBI quality appraisal criteria for qualitative research from the Joanna Briggs Institute (Australia). Results were synthesized using an aggregation approach for meta-synthesis. RESULTS: Ten studies were included. Forty-one themes were extracted, which were grouped into 11 categories and further consolidated into 4 synthesized findings: (1) multidimensional sources of time toxicity; (2) negative impacts of time toxicity; (3) coping strategies adopted to address time toxicity; and (4) positive experiences from investing time in treatment. CONCLUSIONS: Time toxicity has multidimensional effects on cancer patients' daily rhythms, psychological states, and social roles, and requires coordinated attention from healthcare professionals, society, and policy makers. Future efforts should build systemic support through integrating healthcare resources, optimizing service processes, and strengthening social support to reduce patients' time toxicity.
30 May 2026
Read appraisal →Journal of neuro-oncology
Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors
PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities. METHODS: This single-institution retrospective cohort study included patients with metastatic and malignant primary brain tumors who received cranial irradiation as part of their initial tumor-directed therapy. Demographic and treatment characteristics were collected, as well as putative vascular and genotypic risk factors. Univariate, multivariate, and machine-learning analyses were performed using five pre-specified measures of radiation-related toxicity. The primary outcome was change in mini-mental status exam (MMSE). RESULTS: Eighty patients (53% male, mean age 55.7, 53% primary and 44% metastatic brain tumors) were included. Elevated homocysteine and ApOE4 genotype were the strongest predictors of MMSE decline in the multivariate model (OR 3.96 [6.5-200, p < 0.001 and 2.85 [1.92-27.6], p = 0.004). Elevated homocysteine was associated white matter change on MRI and both physician and patient assessment. ApoE4 allele was associated with new endocrine deficiency, and physician assessment. An online nomogram provides risk predictions for each of the five late toxicity outcomes: ( https://afranklin22.shinyapps.io/PRMMSERadiationRiskFactors/ ) CONCLUSION: Two pre-treatment laboratory values (elevated homocysteine and ApOE genotype) were strongly associated with post-radiation neurocognitive and endocrine dysfunction using a variety of domains. Our predictive algorithm can aid clinicians in stratifying baseline risk and should be validated in prospective trials and with additional metrics of ND.
30 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Structured yoga program as supportive care to enhance quality of life in head and neck cancer patients undergoing radiotherapy: a prospective matched controlled study
PURPOSE: Head and neck cancer (HNC) patients undergoing radiotherapy often experience significant deterioration in quality of life (QoL) due to physical and psychological challenges. This study evaluates the effect of a structured yoga program-including physical postures, breathing exercises, and meditation-on QoL among these patients. METHODS: A prospective matched controlled nonrandomized study was conducted at a Tertiary Care Centre in North East India. Then, 140 patients undergoing definitive or adjuvant radiotherapy were equally divided into experimental (yoga intervention) and control groups. QoL was assessed using the EORTC QLQ-C30 questionnaire at baseline, end of treatment, and two months posttreatment. The yoga intervention consisted of thrice-weekly sessions for two months. All primary statistical analysis was done using nonparametric methods. RESULTS: Statistically significant improvement was seen in the experimental group in global health status/QoL (p-value < 0.001), pain (< 0.001), financial difficulties (< 0.001), emotional functioning (0.001), and fatigue aspect (0.024). CONCLUSION: A structured yoga program significantly improved key QoL domains in head and neck cancer patients receiving radiotherapy. Yoga can be considered an effective supportive therapy in this population.
30 May 2026
Read appraisal →Neurosurgical review
Management of pediatric brain arteriovenous malformation: a systematic review of retrospective studies
INTRODUCTION: Pediatric brain arteriovenous malformations (bAVMs) are a major cause of hemorrhagic stroke in children, yet management decisions rely largely on heterogeneous cohorts. Contemporary retrospective studies were systematically reviewed to summarize outcomes across treatment modalities and to characterize interstudy variability. METHODS: PubMed/MEDLINE was searched over the past decade for retrospective pediatric intracranial bAVM cohorts. Eligible studies reported management with observation/conservative care, endovascular embolization, microsurgical resection, stereotactic radiosurgery (SRS), or multimodality therapy. Screened studies had data extracted using a standardized form. Outcomes included obliteration, hemorrhage (first presentation and post-treatment), functional outcomes, complications, and recurrence after apparent cure. Treatment categories were harmonized into mutually exclusive groups for descriptive synthesis. RESULTS: Of 113 identified records, 20 retrospective cohort studies met the inclusion criteria. Hemorrhage was the most common presentation. Across cohorts reporting Spetzler-Martin (SM) grade, low-grade lesions (SM I-II) comprised 740 patients (44.1%), SM III 645 (38.4%), and SM IV-V 293 (17.5%). Treatment strategies varied substantially; among 1,973 classifiable patients, cohorts reported microsurgery-only, SRS-only, endovascular-only, multimodality, and conservative approaches. Definitions of obliteration and assessment methods were inconsistent (DSA-confirmed vs. MRI-based). Microsurgical series reported obliteration rates of 81%-100% in selected populations (unweighted mean ~ 93% across reporting cohorts), whereas SRS-only cohorts demonstrated obliteration rates of 51%-68.5% in the three largest unselected pediatric series (range 36%-84% across all SRS-inclusive cohorts), with latency-period hemorrhage rates of approximately 1.1%-3.2% per series. Recurrence after apparent cure was reported in 7 cohorts, with rates ranging from < 1% to 29%, occurring years after angiographic obliteration. CONCLUSIONS: Contemporary retrospective pediatric bAVM data indicate that modality-specific outcomes are strongly influenced by lesion selection, outcome definitions, and follow-up duration. Recurrence after apparent cure is not uncommon and supports explicit long-term surveillance. Standardized definitions and harmonized reporting are needed to improve interpretability and comparative inference.
29 May 2026
Read appraisal →PLoS medicine
Sequential chemo-immunotherapy followed by standard versus reduced thoracic radiotherapy for older and/or frail stage III non-small-cell lung cancer: A randomized open-label cohort trial
BACKGROUND: The appropriateness of concurrent chemoradiotherapy (cCRT) for older or clinically vulnerable stage III unresectable non-small-cell lung cancer (NSCLC) patients remains contentious. Furthermore, the survival implications of de-escalating thoracic radiotherapy (RT) intensity in this population have not been conclusively elucidated. METHODS AND FINDINGS: We conducted a phase II randomized, open-label, two-cohort (non-comparative) trial at a tertiary hospital in China (NCT05557552). Between September 30, 2022 and April 30, 2024, we enrolled 56 older and/or frail patients with stage III NSCLC who were ineligible for cCRT. The primary endpoint was the 1-year progression-free survival (PFS) rate estimated using the Kaplan-Meier method. Secondary endpoints included objective response rate (ORR), overall survival (OS), and safety. In the intention-to-treat (ITT) set, which included all 56 randomized patients who received at least one dose of study treatment, the 1-year PFS was 84.3% (95% confidence interval [CI] [70.3%, 98.3%]) in the standard RT group and 70.7% (95% CI [54.3%, 87.1%]) in the reduced RT group. In the per-protocol set (53 patients), the 1-year PFS was 82.9% (95% CI [68.9%, 98.8%]) in the standard RT group and 73.4% (95% CI [58.3%, 92.4%]), with a median follow-up of 24 months. Among 56 patients in the safety analysis set, 71.4% of patients experienced grade 3/4 adverse events (AEs) in the standard RT group and 53.6% in the reduced RT group. One patient (3.6%) in the reduced RT and three patients (10.7%) in the standardized RT experienced grade 5 AEs. The main limitations are the non-comparative design, small sample size, and lack of power to establish non-inferiority or superiority. CONCLUSION: The current study suggested that reduced RT combined with sequential chemo-immunotherapy might be feasible for older/frail patients intolerant to cCRT, showing numerically similar survival outcomes. These exploratory findings warrant confirmation in larger, adequately powered randomized trials. TRIAL REGISTRATION: The trial had been registered on ClinicalTrials.gov on Sep 30, 2022. ClinicalTrials.gov NCT05557552.
29 May 2026
Read appraisal →Clinical & experimental metastasis
Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model
Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose selection is often guided by experience rather than individual patient and tumor specific features. A data-driven approach that links dose to quantitative local control and adverse effect predictions could improve planning and follow-up strategies. We performed a retrospective single-center cohort study of lung cancer brain metastases treated with GKRS at the University of Pittsburgh Medical Center (2014-2024). Using routinely available clinical, tumor, and dosimetric variables, we trained a tumor-level Random Survival Forest to predict Local Control and Dose Selection for Lung Cancer Brain Metastases after GKRS treatment. Internal validation used leakage-resistant patient-level grouped cross-validation. Performance was assessed using Harrell's concordance index (C-index) and integrated Brier score (IBS). A connected dose-sweep decision layer evaluated predicted local control across a clinically feasible margin dose grid and selected the dose associated with the most favorable predicted profile at prespecified horizons. The survival model demonstrated good internal performance (C-index 0.83; IBS 0.15). A baseline dose imitation model predicted historical margin dose with low error (OOF MAE 1.59 Gy). The integrated decision layer generated individualized local control trajectories and returned horizon-specific local control probabilities and model-based prescription dose recommendations. An Artificial Intelligence framework integrating time-to-event prediction with outcome-linked dose sweeping can provide individualized GKRS decision support for lung cancer brain metastases by delivering quantitative local control forecasts and model-based dose recommendations.
28 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
Prospective evaluation and clinical outcomes of adaptive radiotherapy for locally advanced non-small cell lung cancer (LA-NSCLC)
BACKGROUND AND PURPOSE: Locoregional tumor control is strongly associated with survival for non-small cell lung cancer, emphasizing the importance of precise radiotherapy delivery. This study aims to evaluate a traffic light adaptation protocol regarding target coverage, protocol performance and patient outcomes. MATERIAL AND METHODS: From 2019 to 2022, we prospectively enrolled inoperable non-small cell lung cancer patients. We acquired daily cone-beam computed tomography and traffic light registrations, classifying anatomical changes and need of action. Using repeated computed tomography (rCT) in week 1 and 3 for dosimetric evaluation, we recalculated clinical target volume (CTV) D98% and V95% with and without adaptation, and compared traffic light registrations with the actual dose distribution. Protocol performance was evaluated by the sensitivity and specificity in detecting CTV V95% < 98%. RESULTS: Among 45 patients with complete registrations, baseline shift and tumor shrinkage were most common changes, leading to corrections from bone to tumor match in 37.8% and replanning in 13.3% of patients. Among 38 patients with at least one rCT, 15.8% would have received insufficient CTV coverage without adaptation. Protocol sensitivity, specificity and balanced accuracy were 83.3, 88.6, and 85.7%, respectively. For 38 stage II-III patients, median overall survival was 43.5 months (95% confidence interval [CI]: 32.9-54.1), median time to locoregional failure 27.4 months (95% CI: 0-61.8) and median estimated time to distant failure 30.7 months (CI not estimated due to censoring). INTERPRETATION: The protocol identified relevant changes, improving target coverage without increasing organ at risk doses. With appropriate training, protocol performance was good, and clinical outcomes were consistent with international results.
28 May 2026
Read appraisal →Journal of radiation research
Feasibility of nonspinal bony landmark-based image registration in CyberKnife ® robotic radiotherapy for pelvic and femoral bone tumors
In stereotactic body radiotherapy for bone metastases using the CyberKnife system, spinal anatomy is generally employed as the alignment reference. However, when treating pelvic tumors such as those in the pubic bone or proximal femur, the spinal reference may be anatomically distant from the target, potentially compromising visualization and localization accuracy. This study evaluated the feasibility of using nonspinal bony landmarks, specifically the pubic bone and femoral neck, as reference points for image registration. Intrafractional positional deviations in six degrees of freedom and the frequency of intrafractional corrections were analyzed according to treatment site, including cases treated with conventional spine-based alignment. Alignment accuracy was visually and numerically verified by the treatment operator before beam delivery. Across all treatment sites, the mean translational and rotational deviations during treatment remained within ±0.5 mm and ± 0.5°, respectively. Furthermore, no substantial differences were observed in the mean frequency of intrafractional corrections among the different alignment approaches. These findings demonstrate that nonspinal tumor-adjacent bony landmarks can provide clinically acceptable accuracy comparable to conventional spine-based registration. To our knowledge, this is the first study to systematically assess the clinical feasibility of this approach in CyberKnife-based pelvic radiotherapy. The proposed method may serve as a practical and reliable alternative to spine-based alignment, particularly when the spine is remote from the target.
27 May 2026
Read appraisal →Journal of radiation research
Feasibility of online adaptive SBRT boost with 1.5-T MR-Linac for cervical cancer patients unsuitable for brachytherapy: a planning study
This study aimed to evaluate the effectiveness of online adaptive treatment (ADT) planning for stereotactic body radiotherapy (SBRT) boost on a 1.5-T MR-Linac in cervical cancer patients ineligible for brachytherapy, compared with the conventional position-alignment (PA) assumed dose distributions. This retrospective analysis included 15 treatment plans from five patients, each receiving 21.0 Gy in three fractions. Daily magnetic resonance imaging (MRIs) were used to compare target and organ-at-risk (OAR) doses between ADT and PA dose distributions. The planning target volume (PTV) was defined as the high-risk clinical target volume (HR-CTV) plus a 3 mm margin. OARs included the bladder, bowel bag, and planning organ-at-risk volume (PRV) for rectum and sigmoid. Reference plans prioritized OAR constraints while maximizing target coverage; the intended PTV D90% range was 18.9-21.6 Gy. PA doses were reconstructed by dose warping based on alignment of the anterior rectal wall between daily and pretreatment MRIs, whereas ADT plans were reoptimized on the daily MRIs. HR-CTV decreased by 1.6%-41.5% between the planning MRI and the first treatment fraction. ADT plans improved PTV D90%, achieved better target coverage and met all OAR constraints, whereas PA plans showed dose constraint exceedance across the three fractions, with mean excess doses of 6.89 ± 4.39 Gy to PRV-sigmoid and 5.76 ± 3.33 Gy to PRV-rectum. In conclusion, 1.5-T MR-Linac based SBRT boost with daily online adaptation enhanced tumor coverage and reduced OAR doses, supporting MR-guided online adaptive radiotherapy as a promising option for patients ineligible for brachytherapy.
27 May 2026
Read appraisal →Physics in medicine and biology
Lattice peak optimization: a mixed-integer framework for geometry-adaptive lattice radiotherapy
Objective.Lattice radiotherapy (LATTICE) delivers spatially distributed high-dose peaks within the tumor volume while maintaining lower doses in surrounding valley regions. Determining feasible peak locations is typically performed using heuristic or manual approaches, which may limit the number and spatial distribution of deliverable peaks. This work introduces a lattice peak optimization (LPO) framework that jointly optimizes peak placement and dose distribution to identify the maximum number of geometrically feasible peaks within the target.Approach.Proton LATTICE planning is formulated as a mixed-integer optimization problem that selects an optimal subset of peaks from a large set of candidate locations within the target. Binary variables represent peak selection and continuous variables model spot weights. The formulation enforces geometric feasibility between peaks while optimizing dosimetric objectives to improve peak-to-valley dose ratio (PVDR) and reduce organs-at-risk (OAR) dose. The resulting nonconvex problem is solved using iterative convex relaxation within an alternating direction method of multipliers framework.Main results.LPO was evaluated on three clinical cases with 150-400 candidate peak locations, from which 4-13 peaks were selected. Compared with 50-90 randomly generated LATTICE configurations per case, LPO consistently achieved higher PVDR and improved OAR sparing. In an abdominal case, the composite objective value was 2.93 (worst random), 2.40 (median random), 1.90 (best random), and 1.95 (LPO), with similar trends observed across all cases.Significance.A geometry-adaptive, mixed-integer optimization framework for lattice peak placement is presented, demonstrating improved PVDR and OAR sparing relative to manual and random LATTICE approaches. The present study evaluates performance of the proposed framework in the context of proton LATTICE planning; however, the framework is mathematically modality-agnostic and could in principle be applied to other radiation modalities.
27 May 2026
Read appraisal →Journal of radiation research
Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions
In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate reference dose distributions as a guideline tool for treatment planners and enabling virtual PBT dose assessment at institutions lacking PBT facilities to support clinical decision-making. This study proposes a dose gradient-aware DL training approach and a beam arrangement-free framework that predicts dose distributions from computed tomography (CT) images and target/organs-at-risk structures without requiring beam arrangement inputs. Using data from 172 HCC patients, we systematically compared 20 DL models combining five architectures with four loss functions, including a novel dose gradient-aware loss capturing PBT-specific dose distribution characteristics arising from the Bragg peak. Prediction accuracy was evaluated using dose metrics for clinical target volume, planning target volume, and normal liver, 10% dose region volume assessment, visual examination, Dice similarity coefficient (DSC) for 10% dose region, and dose-volume histogram (DVH) analysis. DL models employing dose gradient-aware loss demonstrated reduced prediction errors in target volume dose metrics, achieved the lowest errors in 10% dose region volume when combined with U-Net (MAE: 35.4 cm3, RMSE: 51.3 cm3), attained the highest DSC (0.88 ± 0.05), showed the closest DVH agreement with clinical plans for both target structures and normal liver. A comprehensive evaluation consistently confirmed the superiority of dose gradient-aware loss across all metrics, demonstrating the importance of physics-informed, dose gradient-aware learning for accurate PBT dose distribution prediction. Particularly, DL models combining simple architectures with dose gradient-aware loss predicted doses that closely approximated clinical doses in PBT.
27 May 2026
Read appraisal →Journal of radiation research
Feasibility of retrieval-augmented generation for large language models with Japanese input in radiotherapy
Large language models (LLMs) have recently gained attention for their potential. However, concerns remain regarding their reliability due to limitations such as hallucinations and insufficient domain-specific knowledge. Retrieval-augmented generation (RAG) has emerged as a promising approach, enabling LLMs to reference external knowledge sources and generate accurate outputs. We aimed to clarify the potential of RAG-enhanced LLMs with Japanese input in the field of radiotherapy. This was assessed by evaluating performance on three certification examinations in Japan: the Japanese Medical Physicist Examination, the Japanese Board Examination for Radiologists, and the Japanese Board Examination for Radiation Oncologists. In this study, we constructed a RAG system named Rad-Hub, consisting of a Japanese Radiotherapy Knowledge Database (JRKD) and a retrieval framework built on Microsoft Azure. The JRKD was populated with 32 Japanese radiotherapy textbooks and clinical guidelines. We assessed its utility by inputting all multiple-choice questions from the three examinations into ChatGPT-4o, both with and without Rad-Hub, and recording the answers. They were then compared with reference answers determined by experienced medical physicists and radiation oncologists. Rad-Hub improved accuracy across all examinations. Accuracy increased from 77.0% ± 2.6% to 84.6% ± 1.5% in the Medical Physicist examination, from 74.9% ± 2.0% to 82.1% ± 1.1% in the Radiologist examination, and from 55.6% ± 4.4% to 71.7% ± 4.5% in the Radiation Oncologist examination. Performance gains ranged from 7.2% to 16.1%. These findings highlight the potential of RAG-enhanced LLMs, particularly ChatGPT-4o with Rad-Hub, for integration into radiotherapy applications, such as educational and clinical decision assistance.
27 May 2026
Read appraisal →Physics in medicine and biology
Retrofitting a clinical pencil beam scanning proton therapy unit for 2D ultra-high dose rate scanning
Purpose.This work aimed to demonstrate how existing clinical infrastructure can be adapted to enable ultra-high dose rate (UHDR) pencil beam scanning (PBS) for FLASH research. Leveraging available hardware and minimizing modifications, we extended the capabilities of PSI Gantry 1, previously used as a flexible UHDR fixed-beam delivery platform, by enabling true 2D PBS for UHDR small-field irradiations through the addition of a second scanning direction and multiple gantry angles.Methods.A second (vertical) scanning direction was implemented by repurposing an existing steering magnet and integrating it into the control system. Inter-spot dead times were minimized through software optimizations that synchronized control processes and magnet settling times, optimizing the local average dose rate. A precise spot map acquisition process ensured accurate dose delivery across different gantry angles. Additionally, a1kHzlogging system was introduced, enabling the reconstruction of lateral dose and dose-rate distributions from recorded beam parameters.Results.Gantry 1 was successfully commissioned for 2D scanning of small fields (100×24mm2) at gantry angles from 0∘(beam towards floor) to-120∘. Dose-uniform field deliveries were achieved at local average dose rates up to75Gys-1. The reconstructed lateral dose distributions were in good agreement with CCD measurements regarding the90%iso-dose contour, while the dose-rate distributions were validated againstmicro-Diamonddetector measurements, confirming the precision of the recalculated dose rates.Conclusions.By repurposing existing hardware and optimizing beam delivery, Gantry 1 has been upgraded to a true 2D PBS UHDR scanning system, capable of delivering fields at local average dose rates exceeding70Gys-1. The high-frequency logging system enables future reanalysis of data as the mechanisms underlying the FLASH effect become clearer. This transformation makes Gantry 1 a versatile platform for preclinical research and small animal irradiation, advancing the investigation of the FLASH effect.
27 May 2026
Read appraisal →Journal of neuro-oncology
Clinical utility of liquid biopsy in distinguishing true progression from radiation necrosis and pseudoprogression in malignant brain tumor.
BACKGROUND: Distinguishing true tumor progression from pseudoprogression (PsP) and radiation necrosis (RN) following radiotherapy or radiosurgery for intracranial tumors remains a significant clinical challenge. Conventional imaging modalities, including MRI and PET, often lack sufficient diagnostic accuracy, while histopathological confirmation, although definitive, is invasive and not always feasible. Liquid biopsy (LB) has emerged as a promising noninvasive diagnostic approach. METHODS: A systematic review was conducted across PubMed, Embase, Scopus, and Cochrane databases. A total of 964 records were identified, including PubMed (n = 322), Embase (n = 280), Scopus (n = 297), and Cochrane (n = 65), along with three clinical trials identified from trial registers. After removal of 157 duplicates using Covidence, 807 studies were screened by title and abstract. Of these, 749 were excluded, and 58 articles underwent full-text review. Ultimately, 11 studies met the inclusion criteria and were included in the analysis. RESULTS: A diverse range of LB-based biomarkers has been investigated, including immune cell-based markers (e.g., HLA-DRneg/low and VNN2+ CD14+ monocytic myeloid-derived suppressor cells), immune-related proteins (e.g., CXCL11 and MUC-16), circulating tumor cells, cell-free DNA, mitochondrial DNA, B1-SINE elements, microvesicles, and RNA analytes. Plasma was the most used biofluid, with limited evaluation of urine and cerebrospinal fluid. Analytical techniques varied widely, including flow cytometry, PCR-based assays, immunostaining-FISH, and multiplex protein platforms. Several biomarkers showed statistically significant differences among RN, PsP, and tumor recurrence; however, these findings were largely derived from small, heterogeneous cohorts with inconsistent reporting of diagnostic performance metrics. Composite indices, such as the DR-VNN2 index (DVI) and the Necrosis Prediction Index (NPI), showed potential for improving diagnostic differentiation. CONCLUSIONS: LB-based biomarkers show promise for differentiating RN and PsP from tumor recurrence; however, current evidence is limited by small sample sizes, methodological heterogeneity, and lack of standardized diagnostic criteria. Larger prospective studies and validation of composite biomarker models are required to establish their clinical utility. CLINICAL TRIAL NUMBER: Not Applicable.
26 May 2026
Read appraisal →Journal of gastrointestinal cancer
Trend of Adjuvant Radiation in Resected Pancreatic Ductal Adenocarcinoma: Evaluation of RTOG 0848 with a Large National Database
PURPOSE: Adjuvant radiation (AR) remains a controversial treatment modality for pancreatic ductal adenocarcinoma (PDAC). This study aimed to evaluate the findings of the RTOG 0848 trial assessing the benefits of adjuvant radiation in node-negative disease utilizing a national database. METHODS: Utilizing the National Cancer Database, all patients diagnosed with non-metastatic PDAC, who underwent pancreatectomy, were included (2004-2019). The trend of adjuvant radiation utilization was evaluated. Overall survival in a cohort of patients mimicking the RTOG 0848 protocol was examined as were clinicodemographic and pathologic predictors of adjuvant radiation. RESULTS: Overall rates of adjuvant radiation decreased from 45 to 12%, while neoadjuvant radiation rates simultaneously increased from 4 to 12%. Positive margins and nodal disease were the strongest predictors of adjuvant radiation receipt (OR 1.7[ 95% CI: 1.6-1.7] p < 0.001 and OR: 1.1 [95% CI: 1.1-1.2] p < 0.001; respectively). Both high-risk pathologic groups experienced a decline in the use of adjuvant radiation over time, even after exclusion of patients who received neoadjuvant radiation. After application of the RTOG 0848 criteria, improved overall survival was observed in patients with node-negative and positive margins (adjuvant radiation: 28.6 [95% CI: 25.4-31.8] vs 23.0 [95% CI: 19.4-26.7] months; p = 0.005). In the margin positive cohort, adjuvant radiation was significantly associated with reduced mortality (HR: 0.791 [95% CI: 0.7-0.9]; p < 0.001). CONCLUSION: Evaluation of RTOG 0848 in the setting of real-world data indicates a role for adjuvant radiation for patients with margin-positive, node-negative pancreatic cancer. This highlights the essential role of multidisciplinary discussions regarding patients with PDAC care.
26 May 2026
Read appraisal →Head and neck pathology
Digital Pathology in Head and Neck Squamous Cell Carcinoma: Translational Advances and Clinical Integration for Pathologists, Oncologists, and Surgeons
PURPOSE: Artificial intelligence (AI)-enabled digital pathology has advanced rapidly in head and neck squamous cell carcinoma (HNSCC), but its readiness for clinical implementation remains uncertain. This review evaluates the translational maturity of AI-based digital pathology applications in HNSCC and their proximity to routine clinical use. METHODS: A structured literature search of PubMed and Scopus identified studies published between January 2020 and March 2026. Studies were included if they applied AI or machine learning to histopathologic or whole-slide images in HNSCC or related oral malignancies and reported diagnostic, prognostic, biomarker, or treatment-response outcomes. Given the narrative scope of this review, representative studies aligned with key clinical domains were selected for qualitative synthesis. A six-stage translational maturity framework was used to categorize applications based on development stage, validation, and clinical integration. RESULTS: Of 444 identified records, 358 unique studies were screened, from which representative HNSCC-specific AI digital pathology studies were selected for detailed analysis. Most applications-including diagnostic classification, dysplasia grading, prognostic modeling, and therapy-response prediction-remain at early translational stages (Stage 0-1), typically limited to retrospective, single-center cohorts with minimal external validation. Biomarker quantification and HPV prediction show relatively greater maturity, with some studies approaching Stage 2, reflecting external validation and early prospective evaluation. However, calibration, decision-curve analysis, and outcome-linked endpoints are rarely reported. No HNSCC-specific AI pathology tools have demonstrated outcome-linked deployment or achieved regulatory-grade implementation, and evidence for workflow integration, clinical decision impact, and patient-centered outcomes remains limited. CONCLUSION: AI-enabled digital pathology in HNSCC remains an early-stage field. Despite promising technical performance, most applications lack the prospective validation, workflow integration, and outcome-based evidence required for clinical adoption. Progress will depend on multi-institutional prospective studies, standardized reporting of clinical utility, and demonstration that AI-assisted decisions improve patient management and outcomes.
26 May 2026
Read appraisal →PloS one
Timely Integration of specialist palliative care into oncology care for patients receiving radiotherapy for bone metastases: A study protocol for the TIPZO-RT Randomized Controlled Trial
BACKGROUND: Patients living with advanced cancer often benefit from palliative care. Timely referral to specialist palliative care improves quality of life and reduces potentially inappropriate end-of-life care. Despite these benefits, specialist palliative care is frequently introduced late and inconsistently. This study evaluates whether systematically offering a consultation with the hospital palliative care consultation team (PCCT) to all patients referred for radiotherapy for symptomatic bone metastases improves satisfaction with care. PATIENTS AND METHODS: The Timely Integration of Palliative Care in Oncology care for patients referred for palliative RadioTherapy (TIPZO-RT) trial follows the Trials within Cohorts design and is embedded within the PRospective Evaluation of interventional StudiEs on boNe meTastases (PRESENT+) cohort. Following cohort enrollment, 246 patients will be randomized (1:1) to either the intervention or control group. Patients in the intervention group are offered a PCCT consultation, which they may accept or refrain from. Patients in the control group are not informed about the trial and continue to receive usual care. After four weeks, patient satisfaction with care (affective behavior, EORTC Satisfaction with Cancer Care core questionnaire (EORTC PATSAT-C33)) will be compared between the groups. Secondary outcomes include symptom burden, quality of life, overall survival, and palliative care utilization. Additionally, in the intervention group, patients' experiences with the consultation are evaluated. DISCUSSION: Integrating palliative care into oncological care for patients with advanced cancer is essential to deliver comprehensive, patient-centered care that addresses physical, psychosocial and spiritual needs. This pragmatic study may provide evidence to support timely integration of specialist palliative care for all patients with bone metastases who may benefit from specialist palliative care. Using the Trials within Cohorts design, this study generates real-world evidence on the acceptance or need for a consultation with the PCCT, while minimizing disappointment or response bias, as patients in the control group are not informed. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06805396. Registered on 25-03-2025.
24 May 2026
Read appraisal →Journal of radiological protection : official journal of the Society for Radiological Protection
The integration of artificial intelligence in radiation medical physics: insights from an international survey with regional variability
Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving efficiency and precision in imaging, radiotherapy, and nuclear medicine. Nonetheless, its application in the clinical setting is hindered by education, regulation, and ethical issues. To evaluate the views of medical physicists on AI adoption, potential issues, perceived advantages, ethical issues, and training needs. A cross-sectional survey in the form of an online questionnaire was distributed internationally to practicing medical physicists, representing specialties in radiation therapy, diagnostic imaging, and nuclear medicine. The instrument was used to capture demographic variables and AI familiarity, perceived benefits and barriers, ethical issues, training preferences, and future expectations. Descriptive statistics, chi-square tests, and binary logistic regression were performed. Open-ended responses were analyzed using thematic analysis. Most respondents were moderately familiar with AI, but nearly half had not incorporated AI into their practice despite being interested. Adoption patterns differed significantly across geographic regions, with higher usage reported in developed countries. AI familiarity was strongly associated with adoption. The most prominent benefits noted were dose calculation accuracy, patient safety, and workflow efficiency. In contrast, the main obstacles were inadequate training, high implementation costs, absence of standardized protocols, and low availability of advanced equipment. Ethical apprehensions focused on accountability and reduced human oversight. Preferred educational strategies included hands-on workshops and on-the-job training. Regional analysis and modeling revealed variability in AI adoption and identified key predictors, including AI familiarity and specialization. Results indicate that AI is considered an important technology in radiation medical physics. However, implementation was influenced by regional and professional differences. These findings highlight the necessity to expand AI education, create standard training programs, improve infrastructure, and provide clearer governance frameworks to support safe and responsible AI integration.
22 May 2026
Read appraisal →Journal of robotic surgery
Oncologic, functional, and economic outcomes of transoral robotic surgery for HPV-associated oropharyngeal squamous cell carcinoma: a systematic review and meta-analysis of pathology and treatment strategies
The incidence of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) has increased substantially. Transoral robotic surgery (TORS) provides detailed pathological assessment that may support risk-adapted treatment strategies; however, integrated evidence on oncologic, functional, and economic outcomes remains limited. To systematically evaluate oncologic, functional, and economic outcomes of TORS-based treatment strategies in patients with HPV-positive OPSCC. A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. PubMed, Wiley Online Library, MDPI, Cureus, and medRxiv were searched for studies published between 2019 and 2025. Pre-specified subgroup meta-analyses (randomized/clinical trial versus observational studies, and primary versus salvage settings) were performed using random-effects models for overall survival (OS), postoperative bleeding, tracheostomy rate, and long-term gastrostomy dependence. Twenty-two studies (20 clinical and 2 economic) were included, with 14 contributing to quantitative synthesis. The pooled 2-year OS was 94% (95% CI: 0.90-0.98), the 3-year OS was 96% (95% CI: 0.92-0.99), and the 5-year OS was 92% (95% CI: 0.90-0.95). In the salvage setting, the pooled 2-year OS was 76% (95% CI: 0.71-0.80), with no observed statistical heterogeneity, although based on a limited number of studies. Subgroup analyses indicated higher survival estimates in randomized/clinical trial settings compared with observational cohorts. Functional outcomes were generally favorable, with pooled postoperative hemorrhage of 7% (95% CI: 0.04-0.11), tracheostomy rate of 9% (95% CI: 0.03-0.14), and long-term gastrostomy dependence of 2% (95% CI: 0.00-0.03).Across included studies, TORS provided detailed pathological information that may support risk stratification and inform adjuvant treatment decisions; however, the extent of treatment de-escalation varied across studies. TORS appears to be an effective treatment option in selected patients with HPV-positive OPSCC, with generally favorable oncologic and functional outcomes. However, interpretation of these findings is limited by heterogeneity in study design and patient populations. Further prospective studies are required to clarify its role in treatment de-escalation and long-term outcomes.Registration: PROSPERO (CRD420251240194).
22 May 2026
Read appraisal →Postgraduate medical journal
Next-generation cancer therapies: translating experimental advances into clinical practice
Recent advancements in cancer therapy have led to the emergence of innovative approaches that offer new hope to patients. This review provides a comprehensive overview of the latest cutting-edge technologies and strategies in cancer treatment, including clinically approved therapies and experimental modalities in preclinical or early clinical development, with a focus on their potential to improve patient outcomes. We examined next-generation therapies, including personalized immunotherapies, targeted molecular treatments, gene editing approaches, and artificial intelligence-driven strategies. By evaluating the current landscape of these therapies, we highlighted their benefits, limitations, and future directions. The integration of these advanced modalities into clinical practice holds promise for enhancing their efficacy, reducing side effects, and ultimately transforming cancer care.
22 May 2026
Read appraisal →Neurosurgical review
The impact of augmented reality on radiation exposure during spine surgery: a systematic review
BACKGROUND: Augmented reality (AR) has rapidly emerged as an important navigational technology in spine surgery, with growing interest in its potential to reduce dependence on fluoroscopy and mitigate radiation exposure for both patients and operating room staff. METHODS: This systematic review examined current evidence on radiation-related outcomes of AR-assisted spine procedures. A PRISMA-guided search of PubMed, Embase, and Scopus identified twelve eligible studies, including randomized trials, prospective and retrospective clinical cohorts, and cadaveric or synthetic-model investigations. RESULTS: Across the included studies, AR was consistently associated with reduced fluoroscopy use, with several studies demonstrating statistically significant reductions in exposure time compared with conventional fluoroscopy. Two clinical studies directly measured occupational radiation and reported substantially lower staff doses when AR-based navigation and optimized shielding strategies were employed. Patient radiation exposure was similarly decreased in most studies, particularly when AR was integrated with low-dose cone-beam CT protocols. Operative time findings were mixed, reflecting early learning curves and variability in AR systems, but accuracy remained high across platforms. CONCLUSION: Current evidence suggests that AR may reduce intraoperative radiation exposure without compromising workflow or surgical precision, available studies are limited by small sample sizes, heterogenous methodologies, and a paucity of direct staff-dosimetry data. Larger, high-quality multicenter studies are needed to clarify the magnitude of AR's radiation-sparing benefits and to define its role in modern spine surgery.
19 May 2026
Read appraisal →Radiation oncology (London, England)
Comparative study between PET-CT simulation and CT for head and neck cancer treatment and their potential effect on the thyroid gland: a prospective comparative study
BACKGROUND: Radiotherapy (RT) plays a key role in multidisciplinary treatment of Head and neck cancers (HNCs). Over the past two decades, advances have improved RT's tumor control and toxicity profiles. Precise delineation of gross tumor volume and organs at risk using CT and PET-CT is crucial for safe and effective RT. Additionally, RT increases the risk of thyroid dysfunction, especially hypothyroidism. AIM: The present study compared PET-CT simulation and CT-alone planning in head and neck cancer (HNC) treatment, emphasizing their effects on thyroid gland function. METHODS: The patients were divided into two groups according to the simulation method (PET-CT or CT), two fresh blood samples (5 mL/ sample) were collected from each patient, before and after radiotherapy. TSH, FT3, and FT4 (Thyroid biomarkers) were assessed by electrochemiluminescent immunoassay (ECLIA). RESULTS: PET-CT simulation demonstrated a significant improvement in GTVp delineation accuracy by 71.8% compared with CT. After radiotherapy, TSH levels show minor decline changes, whereas FT3 and FT4 levels declined significantly, with greater declines observed in stages II and III. The most common tumor site was the larynx (60% in PET-CT, 75% in CT), followed by the tongue, nasopharynx, and parotid gland. CONCLUSION: PET-CT simulation offered superior GTVp delineation accuracy over CT, improving HNCs radiotherapy planning. Immediately after RT, radiotherapy caused significant declines in serum FT3 and FT4 levels, with only a minor, non-significant TSH declining, particularly in stage II/III disease. Notably FT4 is little more affected in PET-CT simulation patient group, highlighting the need for thyroid function monitoring. These results advocate PET-CT integration in planning and routine endocrine monitoring for treatment complications.
19 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
KaSARi: a national framework for standardized, automated, and predictive radiotherapy in Finland.
BACKGROUND AND PURPOSE: National radiotherapy (RT) data infrastructures are emerging to support treatment quality assurance and outcome research. However, prospective nationwide integration of full DICOM-RT data with toxicity and patient-reported outcome measures (PROMs) remains uncommon. KaSARi (Key advances in Standardizing, Automating, and predicting Risks in RT) was established to create national research infrastructure for prospective RT data collection and integration in Finland. MATERIALS AND METHODS: KaSARi is a prospective, consent-based multicenter cohort infrastructure involving Finnish university and central hospitals. All adult patients receiving RT are eligible. DICOM-RT datasets from treatment planning and oncology information systems are linked to clinician-graded toxicity, PROMs and relevant clinical variables. The infrastructure builds on previously validated multi-institutional DICOM workflows and follows FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Predefined work packages support data harmonization, scalable data processing and enable downstream applications such as AI-based segmentation, dose prediction, and outcome modeling. RESULTS: Patient recruitment started in May 2025. By February 2026, 221 patients had been enrolled at the coordinating center, initially including breast cancer patients and subsequently expanding to all RT indications. Recruitment at a second center began in November 2025, with 37 patients enrolled. The infrastructure is projected to include approximately 29,000 patients over 15 years. Integration of dosimetric, clinical and PROM data is progressing through a phased national implementation as additional centers join the network. INTERPRETATION: KaSARi represents a national infrastructure prospectively integrating full DICOM-RT datasets with clinician-graded toxicity and PROMs across all RT indications within a unified research protocol. The study provides a foundation for nationwide outcome modeling, harmonization of RT practices and development and validation of data-driven AI-based methods.
19 May 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
PET/CT-Guided Neoadjuvant Tislelizumab plus Chemotherapy/Chemoradiotherapy for Resectable Esophageal Squamous Cell Carcinoma: RATIONALE-213 Final Analysis
PURPOSE: The aim of this study is to assess positron emission tomography/computed tomography (PET/CT)-guided neoadjuvant treatment with tislelizumab plus chemotherapy/chemoradiotherapy in patients with resectable esophageal squamous cell carcinoma (ESCC). PATIENTS AND METHODS: RATIONALE-213, a multicenter, open-label, two-cohort phase II study, enrolled 70 patients with resectable ESCC. After one induction chemotherapy cycle, patients were categorized as PET responders (PET maximum standardized uptake value decrease ≥35%) or PET nonresponders (<35%). Subsequently, all patients received three cycles of tislelizumab 200 mg intravenously. In addition, PET responders received chemotherapy and PET nonresponders received chemoradiotherapy as neoadjuvant treatment followed by surgery. The primary endpoint was the pathologic complete response (pCR) rate. RESULTS: As of October 25, 2024 (median follow-up 25.5 months), pCR rates were 30% [95% confidence interval (CI), 11.9-54.3] in PET responders and 34.4% (95% CI, 18.6-53.2) in PET nonresponders. Higher pCR rates were associated with higher baseline programmed cell death ligand 1 tumor area positivity scores in both cohorts. One-year disease-free survival rates were 79% and 74.2%, and event-free survival rates were 87.1% and 67.8%, respectively. Any-grade and grade ≥3 treatment-related adverse events (TRAE) occurred in 93.3% and 50% of PET responders and 97.5% and 82.5% of PET nonresponders, with no new safety signals. The most common grade ≥3 TRAE was decreased neutrophil count (36.7% and 70%, respectively). CONCLUSIONS: Tislelizumab with chemotherapy/chemoradiotherapy as neoadjuvant treatment for resectable ESCC yielded promising pCR rates and favorable survival outcomes with a tolerable safety profile in both PET/CT-guided responders and PET nonresponders.
18 May 2026
Read appraisal →Computers in biology and medicine
Mapping intratumoral heterogeneity through PET-derived washout and deep learning after proton therapy
The distribution of produced isotopes during proton therapy can be imaged with Positron Emission Tomography (PET) to verify dose delivery. However, biological washout, driven by tissue-dependent processes such as perfusion and cellular metabolism, reduces PET signal-to-noise ratio (SNR) and limits quantitative analysis. In this work, we propose an uncertainty-aware deep learning framework to improve the estimation of washout parameters in post-proton therapy PET, not only enabling accurate correction for washout effects, but also mapping intratumoral heterogeneity as a surrogate marker of tumor status and treatment response. We trained the models on Monte Carlo-simulated data from eight head-and-neck cancer patients, and tested them on four additional head-and-neck and one liver patient. Each patient was represented by 75 digital twins with distinct tumoral washout dynamics and imaged 15 min after treatment, when slow washout components dominate. We also introduced washed-out maps, quantifying the contribution of medium and fast washout components to the loss in activity between the end of treatment and the start of PET imaging. Trained models significantly improved resolution and accuracy, reducing average absolute errors by 60% and 28% for washout rate and washed-out maps, respectively. For intratumoral regions as small as 5.1 mL, errors predominantly fell below thresholds for differentiating vascular status, and the models generalized across anatomical areas and acquisition delays. This study shows the potential of deep learning in post-proton therapy PET to non-invasively map washout kinetics and reveal intratumoral heterogeneity, supporting dose verification, tumor characterization, and treatment personalization. The framework is available at https://github.com/pcabrales/ppw.git.
17 May 2026
Read appraisal →Physics in medicine and biology
A novel deep-learning approach for monitoring gastrointestinal air variation during radiotherapy in young patients using radiographs
Objective.To develop deep-learning (DL) predictive models of internal gastrointestinal (GI) gas from radiographs, a surrogate for dose degradation in abdominal proton beam therapy (PBT).Approach.DL architectures (convolutional neural networks and U-Nets) were trained to predict GI air local path length and/or global volume from radiographs. Frontal/lateral radiographs with paired volume and path length labels were simulated from CTs and GI gas segmentations using the Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox. CTs from The Cancer Imaging Archive Paediatric-CT-SEG dataset (N= 349, 0-16 year, North America) were used for model development and internal evaluation; variation in GI filling, organ motion and body size were simulated for augmentation. External evaluation utilised data from two international radiotherapy institutions: one consisting mostly of children and young people (CYP) (N= 21, 2-19 year, Europe); the other predominantly of teenagers and young adults (TYAs) (N= 14, 10-24 year, Asia). Finally, real-life use case was demonstrated on two subjects treated with PBT.Main results.The optimal configuration was the dual U-Net, accurately predicting both volume (mean absolute error (MAE) = 24.0 ± 32.9 ml) and path length (MAE = 0.80 ± 0.59 mm) on the held-out Paediatric-CT-SEG dataset, while also generalising well to external radiotherapy datasets (volume MAE = {18.9 ± 22.4, 34.6 ± 35.2} ml, and path length MAE = {0.52 ± 0.38, 0.67 ± 0.55} mm on the CYP and TYA datasets, respectively). We found a lower Dice similarity coefficient for gas segmentations in TYA vs Paediatric-CT-SEG and CYP (0.56 ± 0.24 vs 0.87 ± 0.09 and 0.84 ± 0.16, respectively), reflecting cohort-specific gas morphology differences. When applied longitudinally, the network detected both monotonic and realistic intrasubject GI gas variation. Proof-of-concept evaluation on clinical radiographs showed promising performance (volume MAE = 29.5 ± 25.9 ml, and path length MAE = 1.10 ± 0.75 mm).Significance.DL-powered radiographs quantified internal GI gas changes with promising performance and generalisability to diverse ages and demographics. This was the first step towards a novel personalised image-guided radiotherapy traffic-light workflow for abdominal PBT with the goal of reducing the need for CBCT to required fractions.
14 May 2026
Read appraisal →Acta oncologica (Stockholm, Sweden)
Improving confidence in MRI-based auto-segmentation via uncertainty assessment.
BACKGROUND AND PURPOSE: Accurate delineation of organs of interest (OOIs, also commonly referred to as organs at risk, OARs) is crucial for safe radiotherapy. While deep learning-based segmentation using convolutional neural networks has achieved high geometric accuracy, clinical translation is hindered by overconfident, uncalibrated predictions in anatomically ambiguous regions. Uncertainty quantification and model calibration are prerequisites for safe clinical workflows. This study compared the standard nnU-Netv2 against its residual-encoding variant (ResEncM), hypothesizing that ResEncM would demonstrate superior reliability and calibration while maintaining comparable geometric accuracy. Patient/material and methods: T1-weighted contrast-enhanced MRI scans from 70 brain cancer patients were used (55 training/validation, 15 testing). Ground-truth contours for brainstem, hippocampi, chiasm, optic nerves, optic tracts, and pituitary were delineated per Danish Neuro Oncology Group guidelines. Both architectures were trained using five-fold cross-validation with identical preprocessing. Epistemic uncertainty was quantified using mutual information, and Expected Calibration Error (ECE) was computed within a 10-mm isotropic margin around reference contours. RESULTS: Both models achieved high geometric accuracy (brainstem dice similarity coefficient [DSC] > 0.93, hippocampi DSC > 0.81). No significant geometric differences were found for large structures. ResEncM showed significantly lower DSC for the pituitary (p = 0.003) and chiasm (p = 0.018). However, ResEncM demonstrated significantly lower epistemic uncertainty and ensemble variance across all structures (p < 0.05), and significantly reduced ECE for the optic chiasm, optic tracts, and pituitary. INTERPRETATION: Integrating a deep residual encoder into the standard U-Net framework significantly improves reliability and calibration of automated brain OOI contours while maintaining strong geometric performance. The ResEncM architecture provides a more trustworthy tool for clinical radiotherapy by reliably flagging high-uncertainty voxels, supporting confidence-aware clinical workflows.
13 May 2026
Read appraisal →Physics in medicine and biology
A delta-based multi-omics model to predict adaptive radiotherapy benefit in patients with non-small cell lung cancer during carbon-ion radiotherapy
Objective.Patients with non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy (CIRT) are potential candidates for adaptive radiotherapy (ART). However, ART is time- and resource-intensive. This study aimed to develop a decision-support tool to estimate ART demand before treatment.Approach.This retrospective study included 69 patients with NSCLC treated with CIRT. Baseline clinical characteristics, dose-volume histogram (DVH) parameters, and radiomic and dosiomic features from the plan-computed tomography (CT) (1-2 weeks before treatment) and confirm-CT (1-2 d before treatment) were collected, and relative change rates in features (ΔDVH, Δradiomics, and Δdosiomics) were calculated. Feature selection involved univariable testing, correlation analysis, least absolute shrinkage and selection operator regression, and stepwise regression with 1000 bootstrap resamples. A multivariable logistic regression model was constructed and validated using 200 iterations of five-fold cross-validation.Main results.ART was triggered in 32 (46.4%) patients. A five-feature delta-omic model (one ΔDVH, two Δradiomic, and two Δdosiomic features) achieved a validated median area under the curve of 0.897 (interquartile range, 0.833-0.950) with good calibration. At a high-sensitivity threshold (0.11), the model identified all ART candidates while potentially reducing the frequency of weekly-CT evaluations in 21.4% of patients; at the maximum-Youden threshold (0.41), the median accuracy reached 78.6%, with up to 50.0% classified as low ART demand.Significance.A delta-omic model predicting ART demand in patients with NSCLC receiving CIRT was developed. The model may help optimize resource allocation and streamline ART workflows, although prospective validation in larger cohorts is warranted before widespread clinical adoption.
13 May 2026
Read appraisal →Neurosurgical review
Effectiveness and safety of proton therapy in intracranial meningioma treatment: a systematic review and meta-analysis
Meningiomas are the most common primary intracranial tumors, often treated surgically. However, complete resection is frequently limited by proximity to critical structures, necessitating adjuvant or definitive radiotherapy. Proton therapy offers dosimetric advantages over photon-based radiotherapy, particularly in sparing adjacent normal tissues. This study aims to systematically evaluate the effectiveness and safety of proton therapy for intracranial meningiomas across tumor grades and clinical scenarios. A systematic review and meta-analysis was conducted according to PRISMA 2020 guidelines using PubMed, EMBASE, Scopus, Web of Science, and Cochrane from inception to November 10, 2025. Studies were eligible if they reported clinical outcomes of proton therapy in ≥ 10 adult meningioma patients. Data extraction and risk-of-bias assessment were performed independently by two reviewers. Pooled complication rates and survival outcomes were calculated using random-effects models. Nineteen studies involving 1,431 patients were included. WHO Grade I tumors comprised 70.6% of cases; Grades II/III made up 25.2% and 4.2%, respectively. The most common proton dose regimens ranged from 13 to 70.2 Gy (RBE). The pooled complication rate was 16% (95% CI 5-27; p < 0.001; I² = 98.5%). Nine studies reported a statistically significant 5-year overall-survival proportion of 91% (95% CI 88-94; p < 0.001; I² = 49.3%). Radiologic local control averaged 71% (95% CI 50-86; I² = 88.2%). Proton therapy provides effective tumor control with acceptable toxicity, especially for low-grade or anatomically complex meningiomas. It is a valuable option for select patients, though further prospective studies are needed to optimize dosing and assess long-term outcomes.
13 May 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Radiographic Progression With and Without Prostate-Specific Antigen Rise in Patients With Advanced Prostate Cancer Treated With Enzalutamide
PURPOSE: In patients who received enzalutamide, we characterized the development of radiographic progression (rPD) without prostate-specific antigen (PSA) rise or progression in metastatic hormone-sensitive prostate cancer (mHSPC) and nonmetastatic castration-resistant prostate cancer (nmCRPC). METHODS: We conducted a post hoc analysis in two phase III trials (N = 2,551; ARCHES [ClinicalTrials.gov identifier: NCT02677896]; PROSPER [ClinicalTrials.gov identifier: NCT02003924]) for co-occurrence of rPD ± Prostate Cancer Working Group 2/3-defined PSA progression, and rPD ± any PSA rise from baseline/nadir by treatment. Kaplan-Meier methods were used for overall survival (OS) outcomes. RESULTS: In ARCHES, 3.5% and 8.5% of 574 patients with mHSPC treated with enzalutamide plus androgen-deprivation therapy (ADT) had no PSA rise or PSA progression at rPD, respectively. Of 79 patients with rPD who received enzalutamide plus ADT, 25.3% had no PSA rise and 62.0% did not meet PSA progression criteria compared with 7.4% and 38.3% of 188 patients with rPD treated with ADT alone, respectively. In PROSPER, 4.4% and 10.3% of 933 patients with nmCRPC treated with enzalutamide plus ADT had no PSA rise or PSA progression, respectively, at rPD. However, of 187 patients with rPD who received enzalutamide plus ADT, 21.9% had no PSA rise and 51.3% did not meet PSA progression criteria compared with 3.6% and 18.8% of 224 patients treated with placebo/ADT, respectively. Liver metastases were >5-fold higher in enzalutamide-treated patients with rPD events versus control, although sample size was small. Compared with no rPD, enzalutamide-treated patients with rPD ± PSA rise or progression had worse OS. CONCLUSION: Given the frequent discordance and poor prognosis of imaging-based progression in the absence of PSA changes during enzalutamide treatment in mHSPC and nmCRPC, periodic surveillance using imaging is recommended.
12 May 2026
Read appraisal →Japanese journal of clinical oncology
Impact of intensity-modulated radiotherapy on survival and pulmonary toxicities for localized esophageal squamous cell carcinoma
OBJECTIVE: Definitive chemoradiotherapy (CRT) is a treatment strategy for localized esophageal squamous cell carcinoma (ESCC). Herein, we aimed to evaluate clinical outcomes of definitive CRT for ESCC. METHODS: We reviewed 127 patients who received definitive CRT for localized ESCC at our institution between January 2004 and December 2022. All patients received elective nodal irradiation with concurrent chemotherapy, primarily comprising cisplatin and 5-fluorouracil, during radiotherapy. Seventeen patients (13%) received intensity-modulated radiation therapy (IMRT) only, and 16 patients (13%) were treated with IMRT as a boost. The median total dose was 61.4 Gy. Approximately 80% of the patients had clinical stage III or higher disease. We analyzed overall survival (OS), progression-free survival (PFS), locoregional recurrence rate (LRR), prognostic factors, and adverse events. RESULTS: The median follow-up period was 13 months for all patients and 22 months for survivors. The 2-year OS, PFS, and LRR were 40.4%, 27.1%, and 30.8%, respectively. The overall complete response rate was 40.2%. On multivariate analysis, clinical stage 0-III (non-T4) (P < .001) and the use of IMRT (P = .034) were significantly associated with better OS. Pulmonary toxicity was significantly lower in the IMRT group (P = .049). CONCLUSIONS: IMRT for localized ESCC may improve prognosis and reduce pulmonary toxicity.
12 May 2026
Read appraisal →Journal of neuro-oncology
cGAS-STING agonists in preclinical glioblastoma animal models: a systematic review of tumor microenvironment modulation and survival outcomes
BACKGROUND/PURPOSE: Glioblastoma (GBM) is characterized by an immunosuppressive tumor microenvironment and poor responsiveness to immune checkpoint blockade, driving interest in cGAS-STING pathway activation to stimulate antitumor immunity. We performed a systematic review of preclinical in vivo studies evaluating STING agonists in glioblastoma, with emphasis on immune microenvironment effects and survival outcomes. METHODS: A systematic review of PubMed, Cochrane, and Embase was conducted through February 2025 following PRISMA guidelines. Studies were included if they evaluated STING agonists in live animal models of GBM and reported survival outcomes or tumor microenvironment changes. Two reviewers independently screened studies and extracted data. Risk of bias was assessed qualitatively based on study design and reporting characteristics. Given heterogeneity in models, treatments, and outcomes, results were synthesized descriptively. RESULTS: Fourteen studies met the inclusion criteria, spanning synthetic and natural cyclic dinucleotides and non-cyclic STING agonists. Across diverse delivery methods, including intracranial injection, hydrogels, and nanoparticle systems, STING agonists were associated with increased CD8 + T-cell and NK cell infiltration and repolarization of tumor-associated macrophages toward pro-inflammatory phenotypes. Several studies reported prolonged survival, including long-term tumor clearance in select models. Combination therapies with immune checkpoint inhibitors or radiotherapy showed synergistic effects in some studies. CONCLUSIONS: STING agonists can enhance anti-tumor immune responses and prolong survival in preclinical GBM models by promoting cytotoxic lymphocyte recruitment and remodeling the tumor-associated myeloid landscape. However, translation remains challenging due to limitations of current models, the limited clinical traction STING modulation has thus far achieved in other cancer types, incomplete understanding of cell-type-specific STING activation in the brain, and the possibility that sustained STING signaling may drive maladaptive inflammation rather than durable antitumor immunity.
12 May 2026
Read appraisal →Physics in medicine and biology
Multi-model study of fast VMAT segment dose calculation with deep learning.
Objective.Deep learning (DL) methods enable photon dose calculation under two main coordinate representations: Beam's Eye View (BEV) and patient coordinates. We evaluate dose calculation accuracy and speed under these coordinate paradigms and with representative DL models within a unified dataset and pipeline, and introduce two lightweight models for fast photon dose calculation.Approach.Planning computed tomography (CT) scans and volumetric modulated arc therapy plans from 24 prostate cancer patients were used. Monte Carlo simulation generated 5940, 540, and 3053 segment doses for training (11 patients), validation (3), and testing (10), respectively. For BEV, we used a combination of convolutional neural network (CNN) and convolutional long short-term memory network (ConvLSTM) called CNN-ConvLSTM, a CNN-Mamba combination (CNN-Mamba), a transformer-based architecture (DoTA), and a cascaded 3D UNet (C3D). These were trained on CT and segment-projection BEV cuboids. For patient coordinates, the DeepDose individual segment dose prediction framework implemented with C3D (DeepDose-C3D) was trained on cropped CT volumes with four physical inputs. Segment and plan dose accuracy were assessed using local gamma passing ratesγPR(2%/3 mm and 1%/3 mm) and dose-volume histogram metrics. Dose calculation times (inference plus pre/post-processing) were measured on three different graphics processing unit (GPUs).Results.All five models achieved mean localγPRvalues⩾91.0% (2%/3 mm) for segment doses and⩾99.0% (1%/3 mm) for plan doses. Mean per-segment dose calculation times were 79, 67, 298, 490, 356 ms for CNN-ConvLSTM, CNN-Mamba, DoTA, C3D, and DeepDose-C3D, respectively. On the latest-generation GPU available, the corresponding per-plan (average 305 segments) dose calculation times were 5.5, 6.2, 33.6, 38.7, 35.4 s.Significance.Both BEV- and patient-coordinate DL methods achieved accurate photon plan dose calculation, with BEV-based approaches showing more robust segment performance. CNN-ConvLSTM and CNN-Mamba retain comparable accuracy at lower computational cost, enabling fast photon dose calculation.
12 May 2026
Read appraisal →PloS one
An exploratory dosimetric and treatment-time analysis of tangent-arc and continuous semi-arc VMAT in deep inspiration breath-hold radiotherapy for stage I left-sided breast cancer
BACKGROUND: The use of the deep inspiration breath-hold (DIBH) technique reduces cardiac and lung radiation exposure during left breast cancer radiotherapy. However, the optimal beam delivery technique and the effects of patient adaptation during DIBH remain incompletely understood. OBJECTIVE: In this study, the dosimetric differences between continuous semi-arc and tangent-arc plans in stage I left-sided breast cancer patients using DIBH were compared, and the treatment session duration was descriptively analyzed to characterize treatment-time trends during routine DIBH delivery. METHODS: Twenty patients treated at our hospital from 01/05/2022-31/05/2023 were retrospectively selected from the institutional database for exploratory dosimetric analysis. Two radiotherapy plans were created on the basis of each patient's computed tomography (CT) images. Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs), and beam-on and total treatment times, were compared. RESULTS: The conformity index (CI) for the PTV was significantly better with the continuous semi-arc plan (P < 0.05), whereas the other PTV parameters did not significantly differ between the plans (P > 0.05). The doses and beam-on time for all OARs (except the left ventricle) were significantly lower for the tangent-arc plan (P < 0.05). Treatment time tended to stabilize across fractions, with a significant difference between the 15th and 16th sessions (P < 0.05). CONCLUSION: With the tangent-arc plan, the beam-on time and radiation exposure to OARs were observed to be lower, while adequate PTV coverage was maintained in patients with stage I left-sided breast cancer using DIBH. Treatment times tended to stabilize with increasing treatment fractions. This observation suggests gradual patient adaptation during routine DIBH rather than a predefined training effect. Given the exploratory nature of these findings and the limited sample size from a single institution, these findings should be interpreted with caution and warrant further investigation in larger, multicenter studies.
11 May 2026
Read appraisal →Journal for immunotherapy of cancer
Phase II trial of combination radiation, hormone, and immunotherapy in grade group 5 prostate cancer
BACKGROUND: Grade group 5 (GG5) prostate cancer (PCa) carries a less favorable prognosis after standard-of-care (SOC) therapy, necessitating novel therapeutic approaches. High-dose rate brachytherapy (HDRBT) and androgen deprivation therapy (ADT) may modulate immune response in GG5 PCa, particularly in tumors with increased immune content. This study evaluated whether the addition of nivolumab to SOC was associated with improved disease control in patients with high-volume GG5 PCa, including those with oligometastatic disease. METHODS: In this non-randomized phase II trial, 31 patients with localized or oligometastatic GG5 PCa and >30% positive biopsy cores were evaluated between September 2018 and April 2021. Patients received four doses of nivolumab (240 mg every 2 weeks) beginning 4 weeks prior to HDRBT, alongside ADT, HDRBT, and external beam radiation. The primary endpoint was to evaluate whether the 2-year freedom from biochemical recurrence (FFBR) rate would exceed a prespecified historical control rate of 75%. RESULTS: Among the 31 patients, the median follow-up was 38.8 months (IQR 31.0-46.5 months). The addition of nivolumab to SOC RT with ADT was associated with a 2-year FFBR rate of 90.3% (95% CI 74.3% to 98.0%) (median FFBR not reached), exceeding the prespecified historical control rate of 75% (one-sided p value from binomial test=0.024). Definitive and probable nivolumab-related toxicity included 6.3% acute grade 2 and 6.3% acute grade 3 adverse events (AEs), with no grade 4+ AEs observed. A higher Decipher immunosuppression score at diagnosis correlated with early pathologic response (p=0.005) and was independently associated with time to metastatic failure (p=0.044). CONCLUSIONS: Nivolumab combined with SOC was associated with encouraging FFBR in this high-risk GG5 PCa population and may represent a promising therapeutic intensification strategy. The Decipher immunosuppression score may serve as a predictive biomarker for response. These findings warrant further investigation in randomized trials.
11 May 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Raman spectroscopy with machine-learning classification for the prediction of stereotactic radiotherapy induced treatment toxicity in high-risk localised prostate cancer
Radiotherapy can lead to late-onset toxicity, to varying extents between individuals due to differences in radiosensitivity. Predicting which patients are most at risk is key to augmenting the therapeutic window. However, the underlying biological mechanisms remain poorly understood, and current experimental methods often lack clinical applicability. This study employs Raman spectroscopy to analyse biochemical profiles in peripheral lymphocytes and plasma, aiming to monitor radiotherapeutic response and predict intrinsic radiosensitivity in high-risk localised prostate cancer patients treated with stereotactic radiotherapy. Partial-least squares discriminant analysis classification of Raman spectra at baseline (n = 20) from post-hormone therapy (n = 19), mid-treatment (pre-4th fraction; n = 21) and 3-months after treatment (n = 18) returned mean area under the curve values ranging from 0.88 to 0.93. Ensemble classifiers applied to imbalanced late toxicity datasets (grade 0-1, n = 16; grade 2+, n = 4) yielded mean F1 scores of 0.74 (random forest, lymphocytes) and 0.69 (AdaBoost, plasma); metrics based on best performing model for minority-class. Classical least squares lymphocyte and plasma toxicity models identified major concentration differences in amino acids, proteins, lipids, DNA and related biomolecules (p < 0.05). These findings demonstrate the potential of Raman spectroscopy as a minimally invasive, objective tool for classifying blood-based biochemical profiles across radiotherapy treatment time points and distinguishing patients with late grade 0-1 and grade 2+ toxicity.
10 May 2026
Read appraisal →Biomedical physics & engineering express
Numerical investigation of neutron beam characteristics for the treatment of deep-seated brain tumour by boron neutron capture therapy
Objective.This study aims to investigate neutron beam characteristics for Boron Neutron Capture Therapy (BNCT) targeting brain tumours and to clarify the physical limitations of single-field irradiation for treatment of deep-seated tumours.Approach.Using Monte Carlo simulations with a voxelated Snyder head phantom, we systematically evaluated the impact of three key characteristics-neutron energy (mono-beam), beam radius, and current-to-flux ratio (J/Φ) -on dose distributions.Results.The analysis identified the 1-10 keV energy range as optimal for depth penetration. Increasing theJ/Φ was found to linearly enhance the tumour dose by improving beam directionality. However, a critical trade-off was observed with beam radius.Significance.These findings demonstrate the fundamental limits of single-field BNCT for deep-seated malignancies and highlight the necessity of multiple-irradiation strategies such as each beam should shape narrow, high-J/Φ epithermal beams. By establishing a database of each beam characteristics provides the essential physical foundation for designing future personalised and multi-beam treatment plans.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Survival modelling of relapse-free survival and competing-risk analysis in patients with high-risk localized prostate cancer treated in GETUG-12.
BACKGROUND: Androgen-deprivation therapy (ADT) plus radiotherapy is a standard of care for men with high-risk localized prostate cancer (PC). Adding docetaxel in this setting demonstrated better relapse-free survival (RFS) but not survival. Few data are available on relapse patterns. Our aim was to investigate whether different patterns of relapses exist in men with high-risk localized PC. PATIENTS AND METHODS: Prospective data from the GETUG12 phase 3 randomized controlled trial comparing ADT alone vs ADT + docetaxel and estramustine (DE) was examined. RFS (main endpoint) was analysed using parametric survival models and second event-free survival (SEFS) defined from biochemical progression (BP) was analysed using a competing-risk approach. RESULTS: Overall, 413 patients were randomized from 2002 to 2006, 206 treated with ADT and 207 with ADT+DE, in addition to local treatment. First analysis showed that the piecewise-exponential model with two time-intervals ([0-3[; ≥3 years) was the model that best characterized RFS with no time-dependent effect of risk factors and treatment. Second analyses limited to patients with a BP showed that the risk of a second event was significantly lower in patients with a longer time-interval from randomization to BP (hazard ratio (HR≥ 3 versus < 3 years): 0.49 [95% CI 0.30-0.79]). In competing-risk analysis, a significant and protective effect of this time-interval was observed for metastases (sub-HR≥ 3 versus < 3 years: 0.41 [0.23-0.73] accounting for salvage treatment. CONCLUSION: Time-interval from randomization to BP is a major prognostic factor for developing local or distant recurrences for patients with high-risk localized PC.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.
PURPOSE: We present MuTriM, a multimodal deep learning model integrating DCE-MRI and whole-slide pathology to predict survival and radiation benefit in breast cancer. PATIENTS AND METHODS: MuTriM employs an attention-based cross-modal and cross-temporal fusion framework that concurrently integrates DCE-MRI radiomic features with pathomic features of cellular morphology on WSI. MuTriM was trained on the Fudan University Shanghai Cancer Center (FUSCC) cohort (N = 335) and externally tested on the TCGA cohort (N = 126) using matched DCE-MRI and WSI. RESULTS: MuTriM was prognostic of recurrence-free survival (RFS) in a subtype-agnostic setting (HR = 5.26, 95% CI, 1.69-16.4, p = 0.004; C-index = 0.75), outperforming unimodal models based on DCE-MRI alone (C-index = 0.65) or WSI alone (C-index = 0.70). MuTriM was also prognostic among HER2 + (HR = 6.82, 95% CI, 1.08-43.2, p = 0.04) and ER+ populations (HR = 4.61, 95% CI, 1.25-17.1, p = 0.02). A high MuTriM score was associated with improved RFS (HR = 0.15, 95% CI 0.03-0.87; P = 0.03), whereas a low MuTriM score showed no significant benefit (HR = 4.06, 95% CI 0.05-31; P = 0.53) (interaction P = 0.04). Transcriptomic analyses revealed that high-risk tumors showed suppressed cytotoxic T-cell activity and enrichment of extracellular matrix remodeling, neutrophil degranulation, and neuronal signaling, consistent with immune evasion. Upregulation of NRXN1, GABRA3, KCNK3, and KLK12 was linked to worse survival, whereas SYNGR3 and CD207 were associated with favorable outcomes. CONCLUSION: By unifying radiology, pathology, and transcriptomics, MuTriM provides an interpretable multimodal framework for breast cancer prognosis and treatment guidance.
10 May 2026
Read appraisal →Signal transduction and targeted therapy
Total neoadjuvant chemotherapy combined with PD‑1 blockade and IL‑2 in MSS/pMMR locally advanced rectal cancer: short-term results of a prospective, single-arm phase II study
Neoadjuvant therapy has become a cornerstone in the management of locally advanced rectal cancer (LARC). In this single-arm, open-label phase II study, we evaluated the efficacy and safety of total neoadjuvant chemotherapy (TNT) using a CapOX regimen combined with a programmed cell death protein 1 (PD‑1) antibody (sintilimab) and interleukin‑2 (IL‑2) in patients with microsatellite stable (MSS)/defining proficient mismatch repair (pMMR) LARC. A total of 33 patients, aged 18-75 years, with rectal tumors located within 12 cm from the anal verge and staged as cT3/4N_anyM0 or cT_anyN⁺M0, were enrolled. Patients received a regimen consisting of oxaliplatin, sintilimab, capecitabine, and IL‑2 administered in a three‑week cycle, with response evaluations performed after every two cycles. Following six cycles of treatment, 33 patients underwent radical surgery, achieving a 100% R0 resection rate. The pathological complete response (pCR) rate was 42.4% (95% CI: 25.68-59.16%) while the remaining 19 patients (57.6%, 95% CI: 41.07-74.09%) were assessed as having a partial response. The tumor regression grades (TRG) were, TRG1: 3 cases (9.1%, 95% CI: 1.90-25.97%), TRG2: 14 cases (42.4%, 95% CI: 26.27-60.38%), and TRG3: 2 cases (6.1%, 95% CI: 0.73-20.37%), respectively. Surgical safety reported as no cases of grade B/C anastomotic leakage or bowel obstruction. Adverse events (AEs) were manageable, with a 21.2% incidence of grade 3 events and no grade IV/V events or treatment‑related deaths. With a median follow‑up of 25.5 weeks, no recurrences were observed. Analysis of blood and tissue samples from patients with different treatment outcomes revealed significant activation of CD8+ T cells, NK cells, and M1 macrophage subsets in the tumor microenvironment of patients achieving pCR. These compelling data demonstrate promising efficacy with a favorable safety profile in MSS/pMMR LARC. These findings warrant studies to validate this regimen as a novel treatment paradigm for rectal cancer. ClinicalTrials.gov registration: NCT06108596.
10 May 2026
Read appraisal →Biomedical physics & engineering express
Dosimetric evaluation of simultaneous multi-energy (2-18 MV) and intensity optimization for IMRT and VMAT.
Objective.This study aimed to develop and evaluate a framework for the simultaneous optimization of beam fluence and multiple photon energies beyond dual-energy configurations for intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT), with the goal of improving treatment plan quality and normal tissue sparing.Approach.An Elekta Versa HD linear accelerator (linac) was modeled using the BEAMnrc/EGSnrc Monte Carlo platform to simulate 2 MV, 4 MV, and 6 MV photon beams, complemented with measured beam data for 10 MV and 18 MV. A custom MATLAB-based framework was developed to perform simultaneous optimization of energy and fluence, allowing energy weighting of individual beamlets. A total of 12 various energy configurations including single (SE), dual (DE), triple (TE), and quadruple (QE)-energy combinations for IMRT and VMAT were generated for thirteen prostate cancer patients. Plans were evaluated using dose-volume histograms, homogeneity index, conformity index, and organ-at-risk (OAR) dose metrics.Main results.Gamma analysis confirmed the high degree of agreement between measured and simulated beam data, which validated our Monte Carlo linac model. Target coverage remained comparable across all energy configurations. Compared with 6 SE, multi-energy (ME) IMRT and VMAT achieved superior OAR sparing, with the greatest benefits for bladder and rectum in the 5-40 Gy range. TE-IMRT reduced bladder and rectal mean doses by up to 1.6 Gy, with V10 reductions of ∼8%; on average, mean doses were reduced by 0.9 Gy (p= 0.001) and 1.0 Gy (p= 0.001). For VMAT, the 6&18 DE-VMAT plan yielded the largest sparing, lowering bladder and rectal mean doses by ∼1.0 Gy. Relative to 10 SE, all ME plans achieved equal or lower OAR doses, with TE-IMRT and 6&18 DE-VMAT showing the most consistent benefit, reducing mean doses to the bladder, rectum, right femoral head, and left femoral head by up to 1.26, 0.77, 0.6, and 0.55 Gy, respectively.Significance.This study presents the first assessment of the feasibility and potential clinical value of simultaneous optimization of beam intensity and more than two photon energies for IMRT and VMAT plans, producing optimized ME fluence maps that require subsequent conversion into clinically deliverable treatment plans. The findings demonstrate that ME optimization can enhance OAR sparing without compromising target coverage, supporting its promise for future clinical application.
10 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Responsiveness and clinical utility of patient-reported outcome instruments in head and neck cancer: a systematic review
PURPOSE: To synthesize and evaluate current evidence on the responsiveness, interpretability (MID/MIC), and clinical utility of validated health-related quality of life (HRQoL) patient-reported outcome (PRO) instruments used in adults with head and neck cancer (HNC). METHODS: A systematic search of PubMed, Scopus, Web of Science, Embase, and Cochrane Library (Central) identified studies published from January 2015 to July 2025. The review was registered in PROSPERO (CRD420251128978) and followed PRISMA 2020 guidelines. Observational and interventional studies assessing HRQoL using validated HNC instruments (EORTC QLQ-C30, QLQ-H&N35/H&N43, UW-QOL, FACT-H&N or MDADI) were included. Eligible studies reported responsiveness (ability to detect meaningful change) or clinical applicability. Methodological quality was evaluated using the COSMIN Risk of Bias checklist, and results were narratively synthesized due to methodological heterogeneity. RESULTS: Thirteen studies involving 8,075 patients met the inclusion criteria. The EORTC QLQ-C30 and H&N35/43, UW-QOL, FACT-H&N, and MDADI exhibited consistent responsiveness, capturing expected HRQoL deterioration during treatment and partial recovery within 6-12 months. Reported minimal important difference (MID) and minimal important change (MIC) values typically ranged from 4 to 15 points for improvement and 10-20 points for deterioration, supporting the interpretability of change scores. Several patient-reported outcome measures (PROMs) also demonstrated prognostic relevance, with early HRQoL changes associated with survival or recurrence. CONCLUSION: Validated HRQoL questionnaires in HNC demonstrate robust responsiveness and meaningful clinical applicability, supporting their integration into routine oncology practice. Their use may enhance early detection of complications, guide rehabilitative interventions, and facilitate more individualized, patient-centered care.
10 May 2026
Read appraisal →European journal of cancer (Oxford, England : 1990)
Stereotactic body radiotherapy versus hepatic resection for recurrent small hepatocellular carcinoma: A post-hoc analysis with propensity score adjustment from two randomized trials
BACKGROUND: Stereotactic body radiotherapy (SBRT) has been proven effective for treating small hepatocellular carcinoma (sHCC); however, the comparative efficacy of SBRT and surgical resection remains unknown. This analysis aims to compare the outcomes of SBRT and resection in treating recurrent sHCC. METHODS: Two randomized controlled trials enrolled recurrent sHCC undergoing resection (NCT01833286) or SBRT (NCT04047173). Patients with single recurrent HCC ≤ 5 cm were included in this study. The primary endpoint was local progression-free survival (LPFS). RESULTS: A total of 158 patients were included, with 83 and 75 patients assigned to the SBRT and resection cohorts, respectively. The cumulative 3-year LPFS rates were 84.3% (95% CI, 76.1-92.5) with SBRT and 76.8% (95% CI, 67.2-86.4) with resection, respectively (HR, 0.70 [95% CI, 0.35-1.40]; P = 0.311). The median progression-free survival time of the SBRT and resection group was 37.6 (95% CI, 26.0-49.2) and 26.3 (95% CI, 18.8-33.8) months, respectively, with no significant differences (HR, 0.80 [95% CI, 0.53-1.21]; P = 0.287). The 3-year overall survival rates were 90.3% (95% CI, 83.4-97.2) in the SBRT group, and 81.1% (95% CI, 76.5-90.1) in the resection group (HR, 0.53 [95% CI, 0.23-1.20]; P = 0.118). The comparison results remained consistent after adjusting for confounding variables. The incidences of adverse events were similar between the two groups (P = 0.695). CONCLUSIONS: SBRT provides satisfactory tumor control and long-term outcomes, comparable to those of resection. SBRT could be a potential alternative to resection in recurrent sHCC.
10 May 2026
Read appraisal →Nuclear medicine communications
Refining deep learning segmentation in gallium-68-prostate-specific membrane antigen-11 positron emission tomography: evaluation of small lesion filtering and intersection-over-union thresholds
PURPOSE: Evaluate how small lesion filtering and different intersection-over-union (IoU) thresholds influence deep learning segmentation of Ga-68-PSMA-11 PET images in prostate cancer. METHODS: A 3D U-Net was trained on 115 patient scans with manual contours as ground truth. Performance was assessed at voxel, lesion and patient levels. Lesions less than 8 voxels (195 mm 3 ) or less than 27 voxels (658 mm 3 ) were optionally discarded, and lesion-level metrics were computed across 10-50% IoU thresholds. RESULTS: Excluding lesions less than 27 voxels increased voxel-level Dice from 0.7975 to 0.8173 and improved precision. Lesion-level metrics were stable across IoU thresholds of 20-40% after 27-voxel filtering, while sensitivity declined at higher overlap thresholds. Patient-level sensitivity and positive predictive value reached 96.6 and 94.5%, respectively. CONCLUSION: Implementing small lesion filtering criteria improves the reliability of automated segmentation outputs, particularly for quantitative evaluation. For lesion-level metrics, defining true positives within the range of 20-40% IoU thresholds is optimal. Further validation through multicenter studies and larger datasets is essential to ensure the generalizability of these findings.
9 May 2026
Read appraisal →Neural networks : the official journal of the International Neural Network Society
MGML: A plug-and-play meta-guided multi-modal learning framework for incomplete multimodal brain tumor segmentation
Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multimodal MRI data are often incomplete, making it challenging to fully utilize the available information. Therefore, maximizing the utilization of this incomplete multimodal information presents a crucial research challenge. We present a novel meta-guided multi-modal learning (MGML) framework that comprises two components: meta-parameterized adaptive modality fusion and consistency regularization module. The meta-parameterized adaptive modality fusion (Meta-AMF) enables the model to effectively integrate information from multiple modalities under varying input conditions. By generating adaptive soft-label supervision signals based on the available modalities, Meta-AMF explicitly promotes more coherent multimodal fusion. In addition, the consistency regularization module enhances segmentation performance and implicitly reinforces the robustness and generalization of the overall framework. Notably, our approach does not alter the original model architecture and can be conveniently integrated into the training pipeline for end-to-end model optimization. We conducted extensive experiments on the public BraTS2020 and BraTS2023 datasets. Compared to multiple state-of-the-art methods from previous years, our method achieved superior performance. On BraTS2020, for the average Dice scores across fifteen missing modality combinations, building upon the baseline, our method obtained scores of 87.55, 79.36, and 62.67 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET), respectively. We have made our source code publicly available at https://github.com/worldlikerr/MGML.
9 May 2026
Read appraisal →Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Validation of a Deep-Learning Coregistration Framework for Long-Axial-Field-of-View PET/CT Using Low-Radiation-Exposure Protocols Across Various Tracers
The advent of long-axial-field-of-view (LAFOV) PET/CT systems has significantly improved whole-body imaging by providing higher sensitivity and extended torso coverage. However, PET/CT potential misalignment remains a challenge, potentially introducing artifacts and quantification errors. Moreover, PET protocols with reduced scanning duration and dose, as well as the use of ultra-low-dose CT (ULD-CT), are increasingly relevant in clinical practice and research. This study aimed to evaluate the robustness and generalizability of a deep-learning coregistration framework for PET/CT alignment using low-dose PET and ULD-CT across various tracers with a LAFOV system. Methods: In total, 63 scans with 4 different tracers (89Zr-trastuzumab, 15O-H2O, 18F-MC225, and 18F-FDG) were included to assess PET and CT alignment improvements. Further evaluation was performed by comparing CT scans coregistered to the original PET images (rCT) with those coregistered to low-count (50%, 25%, and 12.5% of original counts) PET images (LC-rCT). Dice similarity coefficient, Jaccard similarity coefficient, Hausdorff distance, and average surface distance were used as evaluation metrics. Furthermore, ULD-CT coregistered to PET (rULD-CT) was compared with low-dose CT coregistered to the same PET (rLD-CT) using the same metrics. PET accuracy was evaluated by calculating SUVs. Results: The robustness of the deep-learning coregistration framework was demonstrated in reduced PET counts and ULD-CT scenarios across tracers. All metrics indicated robust performance when comparing LC-rCT with rCT and rULD-CT with rLD-CT. Consistent SUVs across varying PET counts and ULD-CT conditions further validated the quantitative accuracy of the approach. Conclusion: This work highlights that neither the use of low-dose PET protocols nor ULD-CT compromise the performance of this coregistration framework across 4 tracers. For LAFOV PET, these findings support the feasibility of such a framework in imaging protocols with reduced radiation exposure.
5 May 2026
Read appraisal →The British journal of radiology
Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance
OBJECTIVES: To develop and validate a deep learning model for whole breast clinical target volume (CTV) contouring and evaluate clinical features affecting its performance. METHODS: Five datasets with 857 patients from a single center were used. Dataset 1 (n = 300) trained and tested the model. Dataset 2 (n = 10) evaluated contouring time and dosimetric parameters. Datasets 3 (n = 20) and 4 (n = 10) were for clinical evaluation. Dataset 5 (n = 517) identified clinical factors influencing auto-contouring accuracy. Model performance was assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). RESULTS: The median DSC and HD95 for left- and right-sided models in Dataset 1 were 0.941, 1.75 mm and 0.937, 2.47 mm, respectively. In Dataset 2, both auto-contouring and auto-contouring with manual corrections were significantly faster than manual contouring (P = .005 for both), while still achieving clinically acceptable dosimetric results. In Dataset 3, two physicians rated automatic and manual contours as equivalent (P = .214, P = .075), while the other rated auto-contouring higher (P < .001). In Dataset 4, the auto-contouring model outperformed 1/5 physicians by DSC (P = .009) and 3/5 by HD95 (P = .015, P = .007, P = .017). In Dataset 5, peripheral tumor-bed and low-density breast tissue were associated with lower DSC (P < .001 for both) and higher HD95 (P < .001 for both). Cases without unfavorable factors performed better than those with (P < .001 for both). CONCLUSIONS: The proposed model demonstrated acceptable accuracy, consistency, and efficiency in breast CTV contouring. Peripheral tumor-bed and low-density breast tissue reduced auto-contouring performance. ADVANCES IN KNOWLEDGE: The characteristics of challenging cases in whole breast CTV auto-contouring should be identified.
5 May 2026
Read appraisal →Journal of medical imaging and radiation sciences
Explainability-informed benchmarking of two deep learning models for organ-at-risk segmentation in MR-guided adaptive radiotherapy
INTRODUCTION/BACKGROUND: Segmentation of gastrointestinal (GI) organs-at-risk (OARs) is a critical yet time-consuming step in MR-guided adaptive radiotherapy (MRgRT), with manual delineation prone to inter- and intra-observer variability. While deep learning approaches have shown promise, their clinical adoption requires not only accuracy but also interpretability and reliability. This study benchmarks two widely used convolutional architectures, U-Net and Residual U-Net (ResUNet), for abdominal OAR segmentation, with an emphasis on explainability-oriented quantitative analysis. METHODS: An anonymized abdominal MRI dataset was used to train and evaluate U-Net and ResUNet using a 5-fold stratified group cross-validation strategy. Segmentation performance was assessed using the Dice Similarity Coefficient (DSC), Intersection-over-Union (IoU), and the 95th percentile Hausdorff Distance (HD95). Explainability was investigated using Gradient-weighted Class Activation Mapping (Grad-CAM) computed from the final convolutional layer of each network. To enable objective analysis beyond qualitative visualization, Grad-CAM activation maps were quantified using numerical localization metrics relative to ground-truth organ masks, including in-organ energy ratio, boundary energy ratio, pointing accuracy, activation Dice coefficient, centroid distance and activation entropy. Grad-CAM metrics were aggregated across gastrointestinal organs and averaged over the five validation folds. RESULTS: Both architectures demonstrated comparable segmentation performance across organs, with no statistically significant differences across evaluated metrics. Grad-CAM analysis showed similar region-level attention patterns, with in-organ activation ratios of 71.4 ± 8.6% for U-Net and 66.2 ± 9.1% for ResUNet, boundary energy ratios of 24.1 ± 4.9% and 21.8 ± 5.2%, respectively, and pointing accuracies exceeding 70% for both models. Uncertainty analysis based on inter-fold variability and boundary error dispersion indicated comparable stability and bounded worst-case behavior. DISCUSSION/CONCLUSION: By integrating performance, uncertainty and explainability quantitative indicators, this study provides an informed benchmarking of two deep learning models for abdominal OAR segmentation. The results suggest that both U-Net and ResUNet exhibit stable and interpretable behavior under the evaluated configurations, supporting their potential use in MR-guided adaptive radiotherapy workflows where reliability and clinical trust are essential.
4 May 2026
Read appraisal →Physics in medicine and biology
FLIP-HEDOS: a patient-specific blood dose quantification model during radiotherapy treatments
Objective.To introduce FLIP-HEDOS, a blood dose modeling framework that combines patient-specific and generic circulatory data to enable individualized blood dose estimation during radiotherapy (RT) treatments and to overcome limitations of non-personalized approaches.Approach.Patient-specific images of vasculature were obtained from patients with thorax-abdomen or head-neck tumors treated with proton or photon RT. Large vessels from the tumor location were segmented, and their blood flow velocities were extracted from phase-contrast MRI. Movement of blood particles (BPs) was simulated using a Lagrangian approach along patient-specific flowlines in these vessels, forming the FLIP method. This circulation was incorporated into a standardized model (HEDOS) to form a closed-loop whole-body model that computes the spatiotemporal BP distribution and associated blood dose accumulation, accounting for dose delivery dynamics, dose rate and beam-on/beam-off times. Cardiac output (CO) was individualized to match each patient blood flow characteristics. FLIP-HEDOS retains the modular HEDOS architecture, extending its large vessel compartments with the FLIP approach patient-specific vessels represented as new compartments. Remaining compartments were adapted to preserve the standardized connectivity of the circulatory network.Main Results.The study quantified mean blood dose values and tracked the number of times BPs entered patient-specific compartments during dose delivery. Simulations were performed across a range of inter-beam intervals, and the three models FLIP (patient-specific vessels), HEDOS (generic whole-body), and FLIP-HEDOS (integrated model) were compared with respect to their resulting blood dose values. The comparison shows that when patient-specific information is incorporated in blood dose assessment, BPs experience higher radiation doses.Significance.FLIP-HEDOS proves that standardized vascular data can be effectively combined with patient-specific parameters to achieve physiologically realistic modeling of circulating blood. By enabling individualized CO and blood flow dynamics, the framework adapts to the anatomical and hemodynamic characteristics of each patient.
3 May 2026
Read appraisal →American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Harnessing Artificial Intelligence for the Management of Patients With GI Cancers
Artificial intelligence (AI) has the potential of reshaping GI oncology by enabling more nuanced interpretation of complex clinical, imaging, and molecular data, while supporting more timely and patient-centered decisions. This article synthesizes perspectives across the GI cancer continuum, beginning with a framework for context-aware AI that emphasizes metadata, multimodal integration, and lifecycle quality and safety as foundations for trustworthy tools that clarify, rather than conceal, uncertainty. Next, AI in endoscopy is highlighted as an example in clinic practice, focusing on computer-aided detection and diagnosis systems that not only increase adenoma detection rates but also raise questions about surveillance burden, real-world effectiveness, and the balance between skill enhancement and potential deskilling of endoscopists. Another section explores how AI can help GI oncologists design, prioritize, and implement highly innovative clinical trials-particularly multi-omic and imaging-driven approaches-while envisioning a future in which far more patients participate in trials that align with their goals and values. The final section reviews emerging AI-enabled clinical trial matching pipelines, including large language model-based retrieval and prescreening tools that operate on real-world electronic health record and protocol data, and discusses challenges related to bias, privacy, explainability, and workflow integration. Together, these contributions argue that the greatest impact of AI in GI oncology will come from deliberately aligning technical capabilities with highly relevant patient-centered clinical questions, ethical governance, and implementation strategies that expand access to trials and improve outcomes for patients with GI malignancies.
2 May 2026
Read appraisal →Clinical & experimental metastasis
All-site radiotherapy for metastatic ewing's sarcoma: a short-term analysis of feasibility, response, and safety.
BACKGROUND: Metastatic Ewing sarcoma (EWS) has a poor prognosis. While metastasis-directed therapy benefits oligometastatic disease, the role of comprehensive all-site radiotherapy in widespread disease remains underexplored. This study aimed to preliminarily assess its short-term efficacy and safety. METHODS: This retrospective analysis included 21 consecutive metastatic EWS patients treated with helical tomotherapy (Aug 2024-Jun 2025). All known metastases and unresected primary tumors received radiotherapy (45-55 Gy in 20 fractions for most sites; 12-45 Gy for lung/pleural metastases). Concurrent systemic therapies included chemotherapy, tyrosine kinase inhibitors (TKIs), immune checkpoint inhibitors (ICIs), or combinations. Primary endpoints were local objective response rate (ORR, RECIST 1.1) at 2 months post-radiotherapy and acute toxicity (CTCAE 5.0). Systemic therapy subgroup analyses were exploratory. RESULTS: Median follow-up was 6 months (range 2-11). Among 77 target lesions, ORR was 61.0% (CR 48.1%), and disease control rate (DCR) was 97.4%. Soft tissue lesions had a significantly higher CR rate than bone metastases (78.3% vs 3.2%, p < 0.001), potentially influenced by RECIST 1.1 limitations and treatment heterogeneity. Median progression-free survival (PFS) was 6.0 months (95% CI: 2.30-9.70), and median overall survival (OS) was 8.0 months (95% CI: 6.69-9.31). Combination systemic therapy was associated with improved PFS (HR 3.94, 95%CI 1.32-11.78; p = 0.014), with chemotherapy-based regimens yielding the best PFS (8.5 months). TKI-containing regimens were linked to shorter PFS (4.0 vs 8.5 months, p = 0.048) due to selection bias. Acute toxicity was manageable, with grade 3 thrombocytopenia (19.0%) and pneumonitis (4.7%) resolving with treatment. CONCLUSION: All-site radiotherapy achieves promising short-term local control with manageable toxicity in metastatic EWS, demonstrating feasibility and a lesion site-specific response warranting further investigation. Systemic therapy, particularly chemotherapy, correlates with improved PFS. Given high systemic progression rates, long-term outcomes may benefit from integration with more effective systemic therapies, supporting future prospective trials.
2 May 2026
Read appraisal →Biomedical physics & engineering express
Kali MC: an open-source toolkit for intraoperative electron radiation therapy treatment planning
Intraoperative electron radiation therapy (IOERT) is commonly delivered without intraoperative imaging, limiting patient-specific planning and requiring fast, reliable treatment calculations in the operating room. As a result, monitor unit (MU) calculations are often performed using spreadsheets and static look-up tables derived from measurements in water. We present Kali MC, an open source, Python-based software developed to streamline IOERT absorbed dose visualization, MU calculation and report generation for treatments delivered with the Liac HWL mobile accelerator. The software provides a graphical interface to visualize precomputed Monte Carlo absorbed dose distributions in water and performs MU calculations using experimentally determined output factors. Optional atmospheric pressure correction and predefined rescaling factors are supported to improve consistency of dose delivery and target coverage with the prescription isodose. Kali MC also generates customizable PDF treatment reports and exports DICOM RT Plan objects to facilitate integration with record-and-verify systems. By integrating validated dosimetric data, correction methods, and 3D dose visualization into a transparent platform, Kali MC supports efficient intraoperative decision-making; however, its use should be locally commissioned and evaluated according to institution-specific procedures and equipment characteristics.
28 Apr 2026
Read appraisal →JAMA
Tumor Debulking in Combination With Chemotherapy in Multiorgan Metastatic Colorectal Cancer: The ORCHESTRA Randomized Clinical Trial
IMPORTANCE: Local therapy, including surgery, radiation, and ablation, is increasingly used in patients with multiorgan metastatic colorectal cancer (mCRC). However, prospective evidence for a survival benefit of tumor debulking is lacking. OBJECTIVE: To investigate whether tumor debulking added to palliative chemotherapy improves survival of patients with multiorgan mCRC. DESIGN, SETTING, AND PARTICIPANTS: This investigator-initiated, open-label, multicenter, randomized clinical trial enrolled patients with multiorgan mCRC between May 2013 and May 2023. The last date of follow-up was April 4, 2024. Patients were enrolled in 27 hospitals in the Netherlands and 1 in the UK. Adult patients with multiorgan mCRC were considered eligible if more than 80% tumor debulking was deemed feasible by resection, radiotherapy, and/or thermal ablation prior to starting first-line palliative chemotherapy. INTERVENTIONS: After achieving objective tumor response or stable disease after 3 cycles of capecitabine and oxaliplatin or 4 cycles of 5-fluorouracil and oxaliplatin with or without bevacizumab, patients were randomized 1:1 to receive chemotherapy alone (standard care group) or tumor debulking followed by chemotherapy. MAIN OUTCOMES AND MEASURES: The primary end point was overall survival. Secondary end points included progression-free survival and serious adverse events. These outcomes were analyzed in the intention-to-treat population, applicable from randomization. A prespecified interim analysis performed after the initial 100 participants were enrolled revealed that the trial was both safe and feasible to proceed. RESULTS: A total of 382 of 454 enrolled patients were randomized: 192 in the chemotherapy alone group (133 [69%] male) and 190 in the chemotherapy plus tumor debulking group (127 [67%] male). The median age was 64 years in both groups. After a median follow-up of 32.3 months, median overall survival in the chemotherapy alone group was 27.5 months vs 30.0 months in the chemotherapy plus tumor debulking group (adjusted hazard ratio, 0.88 [95% CI, 0.70-1.10]; P = .26). Median progression-free survival in the chemotherapy alone group was 10.4 months vs 10.5 months in the chemotherapy plus tumor debulking group (adjusted hazard ratio, 0.83 [95% CI, 0.67-1.02]; P = .08). More patients in the chemotherapy plus tumor debulking vs chemotherapy alone group had any serious adverse events (101 [53%] vs 74 [39%]; P = .006). CONCLUSIONS AND RELEVANCE: Tumor debulking in addition to first-line palliative systemic treatment failed to improve overall survival compared with systemic treatment alone for patients with multiorgan mCRC and should not be considered standard care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01792934.
23 Apr 2026
Read appraisal →JMIR human factors
Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study
BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence of its efficacy comes from controlled settings. There remains a need to complement this knowledge with insights into how AI tools are perceived and used in real-world clinical settings, as well as how their use impacts clinical practice. OBJECTIVE: This study aimed to explore key factors influencing clinicians' acceptance of AI tools and examine how AI adoption and use impact clinical workflows in cancer care. METHODS: We used purposive sampling for recruiting oncology-related health care professionals and collected data using web-based semistructured interviews to gather their perceptions. Data were thematically analyzed and interpreted through the lenses of sociotechnical systems theory and the Unified Theory of Acceptance and Use of Technology. RESULTS: Participants largely accept and perceive AI tools as beneficial to clinical practice. Unified Theory of Acceptance and Use of Technology constructs were reflected in our data as determinants of intention to adopt AI tools. Trust appears as an influential factor in shaping attitudes toward AI tools. Acceptance is found to both precede AI tool use and to grow following successful integration. The use of AI tools is perceived to yield operational benefits, such as reduced workload and time savings, and clinical benefits, such as increased diagnostic reliability and reduced patient recall. Minimal disruption to clinical workflows following integration of AI tools was reported for some cancer screening applications and organ-at-risk segmentation, whereas greater disruption was anticipated for 3D cancer screening. Although accountability and lack of explainability are highlighted in literature as barriers to AI adoption, participants do not view these as significant obstacles in image-based diagnostic contexts. Additionally, negative impacts, such as overreliance on AI and reduced critical review of AI results, arise in association with the use of AI tools. CONCLUSIONS: Participants perceive AI tools to deliver benefits to clinical cancer care. However, their adoption relies on their alignment with clinical needs and seamless integration into clinical workflows. To encourage clinician acceptance, the identified concerns must be addressed. Future work should focus on training programs, co-design with clinicians, and exploration of mitigation strategies for emerging adverse effects, such as automation bias and potential skill erosion.
22 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Pembrolizumab With or Without Lenvatinib as First-Line Therapy for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: Phase III LEAP-010 Study
PURPOSE: The PD-1 inhibitor pembrolizumab is approved as first-line treatment for recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). In LEAP-010 (ClinicalTrials.gov identifier: NCT04199104), the multikinase inhibitor lenvatinib plus pembrolizumab was evaluated as first-line therapy in participants with PD-L1 combined positive score (CPS) ≥1 R/M HNSCC. METHODS: In this phase III, randomized, placebo-controlled, double-blind study, participants 18 years and older with PD-L1 CPS ≥1 R/M HNSCC deemed incurable by local therapy were randomly assigned 1:1 to lenvatinib 20 mg plus pembrolizumab 200 mg IV once every 3 weeks for ≤35 cycles or placebo orally once daily plus pembrolizumab 200 mg IV once every 3 weeks for ≤35 cycles. Primary end points were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Per the prespecified analysis plan, ORR and PFS were reported from the first interim analysis (IA1; data cutoff: July 6, 2022), and OS from IA2 (data cutoff: May 30, 2023). RESULTS: Five hundred eleven participants were randomly assigned to lenvatinib plus pembrolizumab (n = 256) or placebo plus pembrolizumab (n = 255). The median time from random assignment to data cutoff was 11.5 months for IA1 and 21.3 months for IA2. At IA1, the median PFS was 6.2 months for lenvatinib plus pembrolizumab versus 2.8 months for placebo plus pembrolizumab (hazard ratio [HR], 0.64 [95% CI, 0.50 to 0.81]; P = .0001040); the ORR was 46.1% versus 25.4%, respectively (difference = 20.2% [95% CI, 10.5 to 29.6]; P = .0000251). At IA2, the median OS was 15.0 months for lenvatinib plus pembrolizumab versus 17.9 months for placebo plus pembrolizumab (HR,1.15 [95% CI, 0.91 to 1.45]; P = .882). At IA2, 170 (66.9%) participants receiving lenvatinib plus pembrolizumab had grade 3-4 all-cause adverse events compared with 97 (38.3%) participants on placebo plus pembrolizumab. CONCLUSION: In participants with PD-L1 CPS ≥1 R/M HNSCC, first-line lenvatinib plus pembrolizumab significantly improved ORR and PFS, but not OS, compared with placebo plus pembrolizumab. The safety profile was consistent with published data.
21 Apr 2026
Read appraisal →Journal of neurointerventional surgery
Safety and efficacy of selective and superselective intra-arterial cerebral infusion with blood-brain barrier disruption for glioma: a systematic review and meta-analysis
BACKGROUND: Selective and superselective intra-arterial cerebral infusion (SIACI/SSIACI) delivers chemotherapy directly to tumor-supplying arteries, achieving high local drug levels with reduced systemic toxicity. Often combined with blood-brain barrier disruption (BBBd) to enhance penetration, these methods have been widely studied, yet a comprehensive evidence synthesis is lacking. This study systematically assesses the safety and efficacy of intra-arterial chemotherapy with BBBd for glioma treatment. METHODS: We searched PubMed, Embase, and Web of Science for studies on SIACI with BBBd in glioma patients. Safety outcomes included rates of cases with complications, procedure-related complications (major/minor), stroke, intracranial and intratumoral hemorrhage, and mortality. Efficacy was based on tumor response (complete, partial, stable disease, progression). A subanalysis of SSIACI-only cases was also conducted. RESULTS: Nine studies with 230 glioma patients were included. The pooled rate of cases with complications was 27.1% (95% CI 19.8% to 35.7%), with procedure-related complications occurring in 15.4% (95% CI 6.1% to 24.6%). Major and minor complication rates were 4.3% (95% CI 0.9% to 7.7%) and 9.7% (95% CI 1.0% to 18.7%), respectively. Stroke, intracranial hemorrhage, and intratumoral hemorrhage occurred in 3.1% (95% CI 0% to 6.1%), 0.5% (95% CI 0% to 3.2%), and 0.04% (95% CI 0% to 3%), respectively, with no procedure-related deaths reported. Pooled response rates were 10.4% complete (95% CI 0% to 21.5%), 24.2% partial (95% CI 14.3% to 34.1%), 38.2% stable disease (95% CI 21.5% to 54.9%), and 39.3% progression (95% CI 14.8% to 63.8%). Subanalysis of superselective infusions yielded comparable outcomes to the overall cohort. CONCLUSION: The findings indicate that SIACI/SSIACI chemotherapy following BBBd is a feasible and safe approach for glioma treatment, demonstrating a favorable procedural risk profile and preliminary signs of efficacy. Further studies are warranted to validate these results and refine procedural protocols.
18 Apr 2026
Read appraisal →Physics in medicine and biology
A surface-conforming flexible collimator for improving electron radiotherapy.
Objective.We aim to simplify the traditional method of patient-specific skin collimation and to improve the precision of electron radiotherapy treatment with a new surface-conforming collimation system.Method.A surface-conforming collimation device for electron radiotherapy, consisting of parallel steel cables, was created. The cables move freely relative to each other and can be fixed and reshaped with a clamp system along the length of the device. The cables do not completely fill the space; thus, their packing fraction was determined to characterize dosimetric effects. The cable collimator was tested with a 10 × 10 cm cone with four electron energies (6, 9, 12, and 15 MeV) at three SSDs (100 cm, 105 cm, and 110 cm). Radiochromic film was placed at the surface and at the maximum dose depth (dmax) for each energy. Penumbral width, defined as the distance between 20% and 80% of the maximum dose, was measured for each energy and depth.Results.The largest improvement with respect to traditional collimation was seen for 6 MeV with 110 cm SSD at the surface. The penumbra at the surface for the conventional collimator in this case was 18.7 mm, while the cable collimator penumbra was 1.4 mm. The smallest improvement was seen for 15 MeV with 100 cm SSD atdmax(2.7 cm), where the penumbra for the traditional collimator was 11.1 mm, while the cable collimator penumbra was 9.8 mm. In all cases, the cable collimator reduced penumbral width. Surface dose inhomogeneities due to scatter were most pronounced at higher energies. The packing fraction was measured to be 0.551 ± 0.016.Significance.The prototype shows significant improvement in penumbra compared to conventional collimation. The system aims to serve as a replacement for current skin collimation techniques and could be used with Cerrobend applicators for additional surface blocking.
17 Apr 2026
Read appraisal →Physics in medicine and biology
A deep learning-based framework for patient-specific radiation dose prediction in beta-emitting radionuclide therapies
Objective.Conventional image-based models for radionuclide therapy dosimetry are typically radionuclide-specific and rely on nuclear medicine (NM) images for training. We developed a deep learning (DL) model that predicts doses for not only training radionuclides but also radionuclides not included in the training data without patient NM images.Approach.The DL model was trained to predict voxel dose kernels (VDKs) for beta-emitters using CT-derived density maps and energy spectra. The DL model-derived VDKs were convolved with time-integrated activity to generate patient-specific dose maps. Zero-shot prediction was tested on Ho-166 and Lu-177. The performance of DL model was validated at kernel, phantom, and patient levels. The proposed DL model, conventional DL model trained on Y-90 NM/CT images of 22 patients, voxelS-value (VSV), and organ-level dosimetry (OLINDA/EXM) were benchmarked against Monte Carlo simulation (MC).Main results.Training utilized a single CT image, circumventing large patient cohort requirements. The DL model-based VDKs showed strong correlation with MC-based VDKs (R2: 0.99 and 0.91 for test Ho-166 and Lu-177) and, at the phantom level, achieved overall lower errors than VSV in heterogeneous region. At the patient level, the proposed DL model predicted organ doses with the smallest differences from MC while reducing computation time from 4.6 h to 2.6 min. For the test Lu-177, the proposed model exhibited good agreement with MC (2.1 Gy, 11.6%), while the conventional model trained using Y-90 showed large discrepancies (129.4 Gy, 657.4%). For the mean tumor dose averaged across the patient cohort for test Ho-166, the proposed DL model yielded the most accurate results with a difference of only 1.1 Gy (2.9%), outperforming multiple VSV (3.8 Gy, 7.2%), single VSV (3.8 Gy, 7.3%), and OLINDA/EXM (1.4 Gy, 4.5%).Significance.The proposed DL model enables dose prediction for radionuclides not included in the training data without NM images.
16 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
Becotatug Vedotin in Patients with Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck: A Multicenter, Phase IIa Trial
PURPOSE: This multicenter, phase IIa trial (NCT04868162) investigated the efficacy and safety of becotatug vedotin, an anti-epidermal growth factor receptor (EGFR) antibody-drug conjugate, in patients with recurrent or metastatic squamous cell carcinoma of the head and neck (R/M SCCHN) who have limited therapeutic options. PATIENTS AND METHODS: Patients with R/M SCCHN who progressed after platinum-based chemotherapy and/or programmed cell death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors, including those with multiple lines of prior therapy, were administered intravenous becotatug vedotin at either 2 or 2.3 mg/kg every 3 weeks. The primary endpoint was the objective response rate (ORR). RESULTS: Sixty-seven patients were enrolled (35 received 2 mg/kg and 32 received 2.3 mg/kg). The ORR was 20.9% [14/67, 95% confidence interval (CI), 11.9-32.6], with a median duration of response (DoR) of 10.9 months (95% CI, 2.6-15.1). The median progression-free survival (PFS) was 2.9 months (95% CI, 1.8-3.9), and the median overall survival (OS) was 6.7 months (95% CI, 5-8.9). Treatment-related adverse events (TRAE) were reported in 91% (61/67) patients, most commonly being rash (26.9%), pruritus (25.4%), constipation (23.9%), and anemia (20.9%). CONCLUSIONS: Becotatug vedotin demonstrated promising antitumor activity with a manageable safety profile in previously heavily treated R/M SCCHN, particularly at the recommended dose of 2.3 mg/kg, among patients who had failed platinum-based chemotherapy and PD-1/PD-L1 inhibitors (≤2 prior lines of therapy).
16 Apr 2026
Read appraisal →Neurosurgery
Congress of Neurological Surgeons Systematic Review and Evidence-Based Guidelines for the Treatment of Adults With WHO Grade II Diffuse Glioma: Update
BACKGROUND: The management of World Health Organization (WHO) grade II diffuse glioma is an important facet of all physicians involved in neuro-oncology. OBJECTIVE: This is an update of the evidence-based guidelines for management of WHO grade II diffuse gliomas published by the Congress of Neurological Surgeons and American Association of Neurological Surgeons in 2015. METHODS: The medical literature from January 1, 2013, through January 31, 2020, was searched to determine if information was available to update, modify, or create new recommendations related to imaging, surgical approaches, neuropathology and molecular markers, radiotherapy, chemotherapy, and management of tumor recurrence. RESULTS: The writing group used the information from the updated literature search to formulate recommendations based on this evidence and not simply built on biased consensus or expert opinion. CONCLUSION: This series of guideline documents provides an update of the information and recommendations provided in the 2015 version. It sets a benchmark as to the published information we have to support the management of this difficult disease. It also provides clues to key investigations that are necessary to move us toward effective control of WHO grade II diffuse gliomas.
16 Apr 2026
Read appraisal →Clinical cancer research : an official journal of the American Association for Cancer Research
A Multicenter Phase II Randomized Controlled Trial Comparing 177Lu-Dotatate/Capecitabine Combination Treatment with 177Lu-Dotatate Monotherapy in Patients with Neuroendocrine Tumors
PURPOSE: Peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-[DOTA0,Tyr3]octreotate (177Lu-Dotatate) is an effective and safe treatment for patients with metastatic gastroenteropancreatic neuroendocrine tumors (GEP NET). Whereas 177Lu-Dotatate prolongs progression-free survival (PFS) and preserves quality of life (QoL), objective response rates (ORR) remain limited. Capecitabine, as a radiosensitizer, could increase efficacy without increasing 177Lu-Dotatate activity. This phase II randomized controlled trial investigated the additional cytotoxic or radiosensitizing effect of capecitabine in combination with 177Lu-Dotatate. PATIENTS AND METHODS: Patients with advanced somatostatin receptor-positive GEP NET or bronchopulmonary NET were scheduled to receive four cycles of 7.4 GBq 177Lu-Dotatate and capecitabine or 177Lu-Dotatate alone. Capecitabine (1,650 mg/m2/day) was administered for 2 weeks from the start of each PRRT cycle. Primary endpoints were ORR, PFS, and median overall survival (OS). Secondary endpoints included biochemical response, adverse events, and QoL. RESULTS: A total of 111 of the 200 predefined patients were randomized for 177Lu-Dotatate/capecitabine (n = 50) or 177Lu-Dotatate (n = 61). According to the intention-to-treat analysis, the ORR was 32% for the 177Lu-Dotatate/capecitabine group versus 46% for the 177Lu-Dotatate group (P = 0.348). Treatment with 177Lu-Dotatate/capecitabine did not prolong median PFS (45.7 vs. 31.7 months, P = 0.629) or median OS (75.8 vs. 61.4 months, P = 0.530). Quality-adjusted life years (QALY) were lower in the 177Lu-Dotatate/capecitabine group (1.30 ± 0.48 vs. 1.47 ± 0.48, P = 0.014). CONCLUSIONS: In this prematurely closed phase II study, 177Lu-Dotatate/capecitabine did not improve ORR or prolong PFS and OS in patients with advanced NET compared with 177Lu-Dotatate alone and was associated with reduced QALY.
16 Apr 2026
Read appraisal →Computers in biology and medicine
Deep learning with limited data: a transfer learning approach for transcriptomic survival prediction
BACKGROUND: Deep neural networks (DNNs) are promising for analyzing high-dimensional transcriptomic data in cancer research but are limited by data scarcity and heterogeneity. Transfer learning (TL), which leverages large datasets to improve performance on smaller ones, has been widely applied in classification and clustering, yet its use in survival prediction remains underexplored. METHODS: We evaluated transfer learning for disease-free survival prediction using RNA-seq data from 7509 patients across 27 tumor types from The Cancer Genome Atlas. A pan-cancer DNN was optimized via random search across 750 configurations. Tumor-specific baseline models were trained independently, while transfer learning models were built with a leave-one-out pre-training strategy followed by fine-tuning on the target tumor. Performance was assessed with the concordance index. Biological relevance was evaluated through a LIME-like interpretability approach and gene-set enrichment analysis. RESULTS: Transfer learning outperformed tumor-specific baselines in 24 of 27 tumor types, with the greatest improvements in smaller cohorts. Fine-tuning consistently enhanced predictions by adapting pan-cancer features to tumor-specific data. Enrichment analysis confirmed that the models captured both general oncogenic mechanisms and tumor-specific pathways. CONCLUSIONS: Transfer learning improves survival prediction from transcriptomic data, especially in small cohorts, by exploiting molecular patterns shared across cancers. These findings support transfer learning as a robust strategy for survival modeling and a step toward foundation models in molecular oncology.
16 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Mepitel Film versus StrataXRT for the prevention of radiation dermatitis in post-mastectomy radiotherapy: a systematic review and meta-analysis of randomized controlled trials
BACKGROUND: Radiation dermatitis (RD) affects up to 90% of patients receiving post-mastectomy radiotherapy, with moist desquamation causing significant morbidity. Barrier films and dressings such as Mepitel Film and StrataXRT have been shown to be effective compared to moisturizers, but their relative effectiveness remains debated. This systematic review and meta-analysis compares these interventions for preventing RD. METHODS: Following PRISMA guidelines, we systematically searched MEDLINE, Embase, and Cochrane Library through March 14, 2026. An additional grey literature search was performed in clinicaltrials.gov and Google Scholar. Eligible studies were RCTs directly comparing StrataXRT and Mepitel Film in breast cancer patients receiving adjuvant radiation therapy. Two reviewers independently screened studies, extracted data, and assessed risk of bias using RoB 2.0 and certainty of evidence with the GRADE appraoch. Meta-analysis used random-effects models to pool risk ratios (RR) for moist desquamation, and incidence of grade 2 and 3 RD, with I2 statistics quantifying heterogeneity. RESULTS: From 33 screened records, two intra-patient, non-inferiority RCTs (n = 120) met inclusion criteria. Both studies randomized medial/lateral chest wall halves to each intervention in post-mastectomy patients. Pooled analysis revealed no significant difference in moist desquamation incidence between StrataXRT (21.7%) and Mepitel Film (20.0%) (RR 1.03; 95% CI 0.52-2.04; P = 0.93), with low heterogeneity (I2 = 35%). Secondary outcomes showed Mepitel Film significantly reduced grade 2 or 3 RD (RR 1.34, 95% CI 1.10-1.64, P = 0.004). However, Mepitel Film trended toward higher incidence of itchiness (10.8% vs. 5.0%, P = 0.11). Patient preference data were conflicting, with one trial favoring StrataXRT for ease of use and the other showing no clear preference. CONCLUSION: While StrataXRT and Mepitel Film demonstrated similar efficacy for preventing moist desquamation, Mepitel Film offered superior protection against grade 2 to 3 RD. However, this conclusion should be interpreted with caution given the underlying methodological limitations of the included studies, including small and potentially underpowered sample sizes, and variations in radiation fractionation schedules and RT techniques.
15 Apr 2026
Read appraisal →European radiology experimental
Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study
OBJECTIVE: Recently, deep learning (DL)-based reconstruction methods have been introduced into clinical magnetic resonance imaging (MRI) systems to enhance image quality and reduce acquisition time. However, their effects on apparent diffusion coefficient (ADC) maps remain unclear. We investigated whether DL-based image reconstruction influences ADC quantification and histogram-based ADC metrics using a calibrated diffusion-weighted imaging (DWI) phantom. MATERIALS AND METHODS: A phantom containing vials with known ADC values was scanned on a 3-T system using full (fFOV) and reduced (rFOV) field-of-view DWI sequences. Each acquisition was performed using conventional (DL-OFF) and three DL-based strength levels (low, medium, high). Median ADC values were analyzed for repeatability (coefficient of variation (CV)) and accuracy. Histogram changes and first-order radiomic features were assessed using the Wasserstein distance, Friedman, and Wilcoxon tests. RESULTS: ADC estimates showed high repeatability (CV 0.1-1.2%) and good accuracy (deviation -2 to 7%) across all DL levels and sequences. DL reconstruction progressively reduced histogram dispersion, particularly in high-ADC vials. Wasserstein distances increased with DL strength, confirming a progressive effect on ADC value distributions, while median ADC values remained unchanged. Entropy and interquartile range decreased significantly (p < 0.001), whereas kurtosis and skewness increased, with differences showing less stable and sequence-dependent statistical significance. CONCLUSION: DL-based reconstruction maintained accurate and repeatable ADC quantification while reducing the dispersion of ADC values. The effect was more evident for high-ADC regions and the rFOV sequence, resulting in narrower distributions of ADC values. Further investigations comparing different DL-based solutions are warranted to assess the generalizability of these findings in clinical settings. RELEVANCE STATEMENT: Over the past decade, ADC histogram analysis has proven valuable for quantifying tumor heterogeneity, differentiating tumor grade, and evaluating early treatment response. Deep learning reconstruction narrows ADC distributions and reduces dispersion, supporting its potential in oncologic DWI, while highlighting the need for patient-based validation studies. KEY POINTS: DL reconstruction preserved ADC accuracy in both full FOV and reduced FOV DWI. ADC repeatability remained high across DL levels for both DWI sequences. Histogram dispersion progressively reduced across DL levels, particularly in high-ADC vials. Entropy and interquartile ranges decreased progressively with increasing DL strength.
14 Apr 2026
Read appraisal →Signal transduction and targeted therapy
Deutenzalutamide, a novel androgen receptor inhibitor, after progression on docetaxel and abiraterone in metastatic castration-resistant prostate cancer: results from the randomized phase III HC-1119-04 trial
Metastatic castration-resistant prostate cancer (mCRPC) after treatment with docetaxel and androgen receptor signaling inhibitors (ARSIs) has limited treatment options. Although enzalutamide has shown activity after abiraterone and docetaxel, robust evidence from randomized phase III trials is lacking. Deutenzalutamide, a novel derivative with slower metabolism and improved pharmacokinetics, may offer enhanced safety and efficacy. This phase III, double-blind trial conducted at 36 centers in China enrolled patients whose disease progressed on or who were intolerant to abiraterone and docetaxel, or who were ineligible for docetaxel. Patients were randomized (2:1) to receive deutenzalutamide 80 mg once daily or placebo until progression or unacceptable toxicity; the primary endpoint was radiographic progression-free survival (rPFS). Of 417 patients (276 deutenzalutamide; 141 placebo), all had previously received abiraterone, and 68% had also received docetaxel. Deutenzalutamide significantly improved rPFS (HR, 0.58; P = 0.0001), reducing the risk of progression by 42%. Although the initial OS analysis was not significant (HR, 0.95), sensitivity analyses adjusting for subsequent therapies showed significant OS benefits (HR, 0.65-0.73). Treatment-related grade 3 or higher adverse events occurred in 22.3% of patients treated with deutenzalutamide, compared with 15.0% with placebo. The most common treatment-related adverse event was anemia, reported at any grade in 21.2% versus 17.9%, with grade 3/4 anemia in 6.6% versus 2.9%, respectively. Notably, no seizures or falls were reported. In summary, deutenzalutamide significantly prolonged rPFS and, after adjustment, showed a potential OS benefit with a favorable safety profile, supporting its promise as a new treatment option for mCRPC. Clinical trial registration: NCT03851640.
13 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Resistance to neoadjuvant talazoparib in triple-negative breast cancer by BRN2-induced ATR/STAT3 pathways or SHLD2 subclone expansion
Intrinsic and acquired resistance to poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) remains a major barrier in treating homologous recombination (HR) repair-deficient tumors, including those with germline or somatic BRCA1/2 mutations. Although PARPi are FDA approved for adjuvant treatment of locally advanced or metastatic breast cancer in patients with germline BRCA1/2 mutations, emerging data support their use as monotherapy in the neoadjuvant setting. Promising safety profiles of newer-generation PARPi further support this potential. However, resistance mechanisms specific to the neoadjuvant setting are poorly understood. To address this gap, we leveraged resources from a phase II neoadjuvant clinical trial (NCT03499353), analyzing tumors from patients with germline BRCA1/2 mutant breast tumors before and after six months of talazoparib monotherapy. Whole-transcriptome analyses were performed on these samples. Additionally, we established orthotopic patient-derived xenograft models from a subset of the patient tumors and conducted whole-exome and whole-transcriptome analysis. This integrative approach revealed both known and previously unknown PARPi resistance mechanisms. In one case, overexpression of BRN2, encoding a transcription factor that plays a critical role in neurogenesis, led to activation of ATR/RAD51 and STAT3 pathways, restoring HR repair. BRN2-driven resistance could be reversed with ATR and STAT3 inhibitors, resensitizing cells to talazoparib. In another, an HR repair proficient tumor subclone lacking Shieldin 2 expression expanded during treatment and accounted for intrinsic resistance. Our findings highlight the need to determine intrinsic and anticipate acquired resistance pathways in treatment-naïve tumors and support combining PARPi with targeted agents to improve outcomes in the neoadjuvant setting.
12 Apr 2026
Read appraisal →Vaccine
The safety and efficacy of in situ vaccine FOLactis for advanced hepatic malignancies.
BACKGROUND: Recent years have witnessed the emergence of a novel research direction in the field of tumor vaccines. An in situ vaccine known as FOLactis, comprised of genetically engineered Lactococcus lactis expressing a fusion protein of Fms-like tyrosine kinase 3 ligand (Flt3L) and co-stimulator OX40 ligand (OX40L), was developed by the Oncology Centre of Nanjing Drum Tower Hospital. The present study was designed to evaluate the efficacy and safety of combination therapy based on intratumoral injection of the in situ vaccine FOLactis and intravenous PD-1 monoclonal antibody in combination with immunoradiotherapy or low-dose chemotherapy in the treatment of advanced hepatic malignancies. METHODS: In this exploratory clinical study, patients with advanced hepatic malignancies were enrolled and randomly assigned to Group A and Group B, corresponding to different treatment regimens. The treatment regimen in Group A consisted of the in situ vaccine FOLactis combined with a PD-1 monoclonal antibody and immunoradiotherapy (5Gy/fraction×3fractions). In contrast, the regimen in Group B included the in situ vaccine FOLactis, a PD-1 monoclonal antibody, and low-dose chemotherapy (cisplatin plus vincristine). All treatments were given in 21-day cycles. The primary observation indicator of the study was the objective response rate (ORR) as defined by RECIST v1.1. Secondary observation indicators included the disease control rate (DCR) and the incidence of adverse events. RESULTS: A total of 18 eligible patients were included in the study. In Group A, the ORR and DCR for targeted lesions were 33.3%(95%CI:12.1-64.6%) and 77.8%(95%CI:45.3-96.1%), while the ORR and DCR for non-targeted lesions were 22.2%(95%CI:3.9-54.7%) and 66.7%(95%CI:35.4-87.9%), respectively. Targeted and non-targeted lesions in Group B did not reduce significantly, and their respective DCRs were 66.7%(95%CI:35.4-87.9%) and 22.2%(95%CI:3.9-54.7%). Patients in Group A experienced no significant adverse events. Patients in Group B experienced grade 1-2 hyperthermia as a side effect. CONCLUSIONS: The combination therapy based on the in situ vaccine FOLactis and intravenous PD-1 monoclonal antibody showed a certain degree of anti-tumor effect, and was safe and well tolerated in patients with advanced hepatic malignancies. TRIAL REGISTRATION: The Ethics Committee of Nanjing Drum Tower Hospital approved this study (approval number 2022-101-01). The associated trial is registered at ChiCTR.gov.cn (ChiCTR2200060660).
12 Apr 2026
Read appraisal →Biomedical physics & engineering express
The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation
Purpose. To investigate how annotation consistency influences deep learning-based auto-contouring performance for organs-at-risk (OARs) in nasopharyngeal cancer radiotherapy.Methods. We evaluated CT scans from 1,301 nasopharyngeal carcinoma patients: 65 contoured by Physician A, 76 by Physician B, and 1,160 by heterogeneous multi-physician teams. Three cohorts (50 samples each for Physicians A/B; 1,000 for multi-physician) with standardized U-Net training protocols generated Models A, B, and C. Model C underwent physician-specific fine-tuning. Performance was quantified via Dice similarity coefficients (DSC) against ground-truth contours across 14 critical OARs.Results. Each model achieved peak accuracy on physician-matched test data. Critically, small-consistency models (A/B) outperformed large-heterogeneous Model C on target cohorts (Model A: 0.777 versus Model C's 0.743 on Test A; Model B: 0.806 versus 0.765 on Test B). Physician-specific fine-tuning closed institutional data gaps, boosting Model C's DSC to 0.795 (+7.08% versus original) on Test A and 0.814 (+6.32%) on Test B-surpassing both original Model C and dedicated small-data models (Model A: +2.39%; Model B: +0.97%).Conclusion. Annotation consistency supersedes dataset scale as the primary performance driver for OAR auto-contouring. Small high-consistency datasets enable optimal native model accuracy, whereas fine-tuning large pre-trained models with targeted physician data closes domain adaptation gaps and delivers state-of-the-art segmentation, advancing precision radiotherapy for head and neck oncology..
11 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Development of a Classifier for Metabolic Subtypes of Nasopharyngeal Carcinoma to Guide Personalized Immunotherapy Strategies: Biomarker Analysis of the Phase III CONTINUUM and DIPPER Trials
PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We aim to identify biomarkers predictive of immunotherapy benefits, using data from the phase III CONTINUUM (ClinicalTrials.gov identifier: NCT03700476) and DIPPER (ClinicalTrials.gov identifier: NCT03427827) randomized clinical trials. PATIENTS AND METHODS: Tumor samples from 407 patients in the CONTINUUM (discovery cohort) and DIPPER (validation cohort) trials were subjected to RNA sequencing. In the discovery cohort, metabolic gene-based consensus clustering was performed to determine subtypes. A machine learning-based classifier was subsequently developed in the discovery cohort and then applied to the validation cohort to assign metabolic subtypes. Gene set enrichment analyses were used to characterize the biological features of each metabolic subtype. The clinical end point was event-free survival (EFS). RESULTS: In the discovery cohort, three metabolic subtypes were identified with distinct tumor-intrinsic and immune features as well as differential EFS benefits from adding anti-PD-1 to chemoradiotherapy (CRT). Specifically, the MS1 subtype exhibited a significant improvement in 3-year EFS in the anti-PD-1 plus CRT arm compared with the CRT-alone arm (3-year EFS, 90.2% v 69.6%; hazard ratio, 0.27 [95% CI, 0.11 to 0.67]), whereas MS2 (3-year EFS, 94.1% v 93.8%) and MS3 subtypes (3-year EFS, 75.0% v 75.0%) derived no significant survival benefit. The subtype features were preserved in the validation cohort, with consistent prognostic and predictive value. A pooled analysis of both cohorts demonstrated the significant interaction between metabolic subtypes and the treatment effect (Pinteraction = 0.0074). CONCLUSION: In this biomarker study, we defined metabolic subtypes of NPC that predicted the EFS benefit from immunotherapy. This novel molecular classification provides a promising predictive biomarker for personalized treatment decision for patients with locoregionally advanced NPC.
11 Apr 2026
Read appraisal →PloS one
Development and evaluation of a multimodal feature-based predictive model for radiotherapy-induced oral mucositis in nasopharyngeal carcinoma
BACKGROUND: Accurate prediction of radiation-induced oral mucositis is crucial for personalized treatment in head and neck cancer. However, developing robust predictive models utilizing high-dimensional multimodal data (CT imaging, dose distribution, and clinical features) remains challenging, particularly in cohorts with limited sample sizes. OBJECTIVE: This study aimed to rigorously evaluate and compare the multi-class predictive performance of traditional machine learning algorithms and deep learning architectures under a small-cohort setting. METHODS: Multimodal data from 108 patients were collected. A comprehensive evaluation framework incorporating nine traditional machine learning algorithms and two deep learning models (a dimensionality-reduced 1D-CNN and a multimodal 3D-CNN) was established. To ensure robust evaluation, a stratified 5-fold cross-validation was employed. Model performance was comprehensively quantified using mean ± standard deviation (SD) across multiple metrics, including the Area Under the Curve (AUC), accuracy, and Matthews Correlation Coefficient (MCC). RESULTS: Inter-rater reliability for RIOM grading was excellent (Cohen's kappa = 0.82, 95% CI: 0.73-0.91). Among traditional machine learning approaches, the Extra Trees (ET) algorithm achieved the highest discriminative capacity (AUC: 0.956 ± 0.046), while Logistic Regression (LR) demonstrated optimal overall accuracy (0.832 ± 0.155) and stability. Regarding deep learning, the lightweight 1D-CNN utilizing fused low-dimensional features exhibited highly competitive and robust performance (AUC: 0.900 ± 0.072; Accuracy: 0.732 ± 0.140). In stark contrast, the high-dimensional multimodal 3D-CNN suffered from severe overfitting and mode collapse phenomenon, yielding significantly inferior results (AUC: 0.568 ± 0.090; MCC: -0.025 ± 0.031). CONCLUSIONS: For small-cohort radiomics and dosimetric analyses, ensemble learning models (e.g., ET) and appropriately regularized linear models (e.g., LR) remain highly effective. While deep learning holds promise, high-dimensional architectures like 3D-CNNs are highly susceptible to mode collapse without massive datasets. Instead, employing feature dimensionality reduction combined with lightweight networks (1D-CNN) is a vastly superior strategy to extract reliable predictive patterns from limited clinical data.
10 Apr 2026
Read appraisal →JMIR cancer
Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice
BACKGROUND: Artificial intelligence (AI) is rapidly reshaping oncology, offering advancements in clinical care and patient support. A growing area of interest is the integration of empathic AI: systems integrating clinical precision with emotional intelligence to support medical decision-making and the emotional and psychosocial well-being of patients and caregivers. OBJECTIVE: This review aimed to explore the role of empathic AI in cancer care, with a focus on its applications in patient education, clinician support, and emotional care. It also evaluated the ethical, cultural, and implementation challenges associated with its integration into clinical practice in oncology. METHODS: A systematic search of literature was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Peer-reviewed papers published between January 2018 and January 2025 were identified through a search of PubMed, Scopus, and IEEE Xplore, and citation tracking. Eligible studies focused on applications of empathic AI in oncology. A total of 44 studies were included and analyzed thematically across 3 core clinical domains: tailored communication and education, diagnostics and care plan optimization, and emotional and psychosocial support. RESULTS: Empathic AI demonstrates the potential to improve cancer care by enhancing patient education, clinical decision-making, and emotional support. Common applications include personalized education for patients and providers, support for diagnostic and therapeutic decisions, and tools designed to recognize and respond to patient distress. Several studies noted improved patient engagement and reduced clinician burden. However, concerns were identified regarding overreliance on AI systems, cultural insensitivity, and patient privacy. CONCLUSIONS: Empathic AI represents a promising advancement in patient-centered oncology, integrating emotional intelligence into clinical care. Its successful implementation will require careful attention to ethical, cultural, and clinical considerations to ensure health equity, trust, and safety in AI-assisted cancer care.
10 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Neoadjuvant Chemotherapy With CAPOX Versus Chemoradiation for Locally Advanced Rectal Cancer With Uninvolved Mesorectal Fascia (CONVERT): Final Results of a Phase III Trial
PURPOSE: The neoadjuvant chemoradiotherapy (nCRT) might accentuate surgical complications and toxicity in the treatment of locally advanced rectal cancer (LARC) while neoadjuvant chemotherapy (nCT) alone shows promise as an alternative treatment. However, which patients deserve most from the nCT need further clarify. This trial aimed to assess the non-inferiority of nCT with capecitabine plus oxaliplatin (CAPOX) versus nCRT with capecitabine in LARC with uninvolved mesorectal fascia (MRF). METHODS: Patients with LARC within 12 cm from the anal verge and uninvolved MRF were randomly assigned to receive 4 cycles of CAPOX chemotherapy alone (nCT group) or CRT with concurrent Capecitabine (nCRT group). The primary end point is 3-year locoregional recurrence-free survival (LRRFS). Secondary end points, such as 3-year disease-free survival (DFS), 3-year overall survival (OS), and adverse events (AEs), were also reported. RESULTS: A total of 663 patients were enrolled and 589 patients received the allocated treatment (nCT, n = 300; nCRT, n = 289). LRRFS was analyzed with a median follow-up of 48 months. 3-year LRRFS was 97.4% (95% CI, 95.5 to 99.3) in the nCRT group and 96.3% (95% CI, 94.0 to 98.6) in the nCT group, resulting in a hazard ratio (HR) of 1.40 (95% CI, 0.53 to 3.68). The nCT and nCRT achieved similar 3-year DFS (89.2% v 87.9%; HR, 0.88 [95% CI, 0.54 to 1.44]) and 3-year OS (95.0% v 94.1%; HR, 0.86 [95% CI, 0.42 to 1.76]). The nCT group showed a lower incidence of grade 2 to 4 long-term AEs (16.0% v 26.3%, P = 0.002) and proctitis (33.6% v 41.7%, P = 0.049) compared with nCRT group. CONCLUSIONS: The non-inferiority of nCT was not confirmed with a very low incidence of local recurrence in both group. But nCT offers comparable DFS and OS while mitigating the burden of toxicity as compared to nCRT. These insights shed light on a potential paradigm shift in the treatment for LARC with uninvolved MRF.
10 Apr 2026
Read appraisal →Biochemical and biophysical research communications
Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches
Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dismal (median survival ∼15 months) and current prognostic models often function as "black boxes," lacking interpretability and limiting clinical utility. There is an urgent need for interpretable prognostic tools to better stratify GBM patients. This study performed cross-cohort, cross-platform transcriptome data integration (TCGA RNA-seq and GEO microarrays) and incorporated inferred immunogenomic features to capture GBM's complexity. An automated machine learning (AutoML) pipeline tested over 100 algorithmic combinations to build an optimal survival prediction model. The final model is a 22-gene signature, and SHAP (SHapley Additive exPlanations) analysis was applied to explain each gene's contribution to risk. Key genes identified by the model (e.g. UBE2W, EID1, HS2ST1) were validated by qRT-PCR and Western blot, confirming their dysregulated expression in GBM cell lines. The 22-gene model achieved a concordance index of ∼0.72 and was validated on independent cohorts (TCGA training and GEO validation), demonstrating robust performance. It effectively stratified patients into high- and low-risk groups with significant survival differences. High-risk tumors were associated with an immune-cell-enriched yet immune-evasive microenvironment, showing greater infiltration of immunosuppressive cells and higher TIDE scores (indicating immune escape). In contrast, low-risk patients had a more favorable immune profile, and their tumors were predicted to be more sensitive to multiple chemotherapeutic agents. This interpretable transcriptome-based integrative prognostic model can serve as a valuable tool for GBM risk stratification and may guide therapeutic decision-making by highlighting potential targets. Not only does it improve outcome prediction, but it also identifies novel prognostic biomarkers, holding promise for personalized treatment and clinical translation in glioblastoma.
10 Apr 2026
Read appraisal →Physics in medicine and biology
Fraction-variant VMAT planning for patients with complex gynecological and head-and-neck cancer.
Objective. Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient discomfort and increase intra-fractional motion. In this study, it was investigated whether the delivery of different VMAT plans in different fractions may improve the dosimetric quality and delivery efficiency for the treatment of patients with complex tumor geometries.Approach. A direct aperture optimization algorithm was developed which allows for the simultaneous optimization of different VMAT plans to be delivered in different fractions, based on their cumulative physical dose. Each VMAT plan is constrained to deliver a uniform dose within the target volume, such that the entire treatment does not alter the fractionation scheme and is robust against inter-fractional setup errors. This approach was evaluated in-silico for five patients with gynecological and five patients with head-and-neck cancer.Main results. For all patients, fraction-variant treatments achieved significantly better target coverage and reduced the dose to critical organs-at-risk compared to fraction-invariant treatments that deliver the same plan in every fraction, where the dosimetric benefit was shown to increase with the number of different plans to be delivered. In addition, 1-arc and 2-arc fraction-variant treatments could approximate the dosimetric quality of 3-arc fraction-invariant treatments, while reducing the delivery time.Significance.Fraction-variant VMAT treatments may achieve excellent dosimetric quality for patients with complex tumor geometries, while keeping the delivery time per fraction viable.
9 Apr 2026
Read appraisal →Signal transduction and targeted therapy
Anti-PD-1 antibody penpulimab plus chemotherapy for recurrent or metastatic nasopharyngeal carcinoma: a randomized, double-blind phase 3 study
In this phase 3 trial, penpulimab combined with chemotherapy was assessed against a regimen of placebo plus chemotherapy for the first-line treatment of recurrent or metastatic nasopharyngeal carcinoma (R/M NPC). 291 patients were randomised and allocated in a 1:1 ratio in order to receive penpulimab (n = 144; 200 mg) or placebo (n = 147; 200 mg), plus chemotherapy (cisplatin/carboplatin and gemcitabine) every 3 weeks. Patients followed by maintenance therapy with penpulimab or placebo after 6 cycles. The primary endpoint of this study was progression-free survival (PFS) according to RECIST v1.1, and a significantly longer median PFS in the penpulimab arm versus the placebo arm (9.63 versus 7.00 months; hazard ratio 0.45, 95% CI: 0.33-0.62, P < 0.0001) was demonstrated in this prespecified interim analysis. The key secondary endpoint was the overall survival (OS). However, the OS data were still immature, and the median OS was not achieved (hazard ratio, 0.94; 95% CI: 0.63-1.40). The occurrence of treatment-related adverse events (grade ≥ 3) was 89.0% and 85.9% in two arms, with the most common being reduced the quantity of neutrophil (56.2% vs. 62.0%), reduced the quantity of white blood cell (54.1% vs. 54.9%), and anemia (45.2% vs. 38.7%). In the penpulimab arm, 6 patients (4.1%) experienced immune-related adverse events (grade ≥ 3). Adding penpulimab to chemotherapy led to a notable enhancement in PFS for the first-line R/M NPC treatment, alongside a safety profile that was both manageable and tolerable. ClinicalTrials.gov identifier NCT04974398.
8 Apr 2026
Read appraisal →JAMA
Treatment for Brain Metastases With Stereotactic Radiation vs Hippocampal-Avoidance Whole Brain Radiation: A Randomized Clinical Trial
IMPORTANCE: Brain metastases are common in patients with cancer, and radiation is often used for management. Among patients with more than 4 brain metastases, the effects of stereotactic radiation targeting only individual tumors, compared with whole brain radiation with hippocampal avoidance, which radiates both tumors and normal brain, remain unknown. OBJECTIVE: To determine whether stereotactic radiation improves symptom severity and interference with daily functioning, compared with whole brain radiation with hippocampal avoidance. DESIGN, SETTING, AND PARTICIPANTS: Phase 3, open-label, randomized clinical trial conducted at 4 United States-based centers. Eligible patients had 5 to 20 brain metastases and no prior brain-directed radiation. Enrollment occurred between April 11, 2017, and May 17, 2024 (final follow-up, March 18, 2025). INTERVENTION: Stereotactic radiation, compared with whole brain radiation with hippocampal avoidance. MAIN OUTCOMES AND MEASURES: Mean weighted patient-reported symptom severity and interference score change over 6 months postbaseline relative to baseline using the MD Anderson Symptom Inventory-Brain Tumor instrument (scale, 0-10; score change range, -10 to 10; -10 = best). A clinically meaningful Δ was defined as 0.98. RESULTS: Of 196 randomized patients (mean age, 61 years; 129 [66%] female; 176 [90%] White; median number of brain metastases, 14 [IQR, 11-18]; 49 [25%] with prior neurosurgical resection), 83 (42%) completed the 6-month assessment. For the primary outcome, between baseline and postbaseline assessments through the 6-month follow-up, stereotactic radiation changed the weighted composite MD Anderson Symptom Inventory-Brain Tumor score from 2.69 to 2.37 (mean change, -0.32) and hippocampal-avoidance whole brain radiation changed the score from 2.29 to 3.03 (mean change, 0.74) (mean difference, -1.06 [95% CI, -1.54 to -0.58]; P < .001). Related grade 3-5 adverse events occurred in 12 patients (12%) in the stereotactic radiation group and 13 patients (13%) in the hippocampal-avoidance whole brain radiation group; grade 1-3 fatigue was most frequent (27 [28%] vs 43 [44%], respectively). CONCLUSIONS AND RELEVANCE: In patients with 5 to 20 brain metastases, these findings support stereotactic radiation over hippocampal-avoidance whole brain radiation to improve symptoms and interference with daily functioning, key components of quality of life. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03075072.
8 Apr 2026
Read appraisal →Physics in medicine and biology
End-to-end testing of an online adaptive radiotherapy workflow for liver tumors using a dynamic Thoraco-abdominal anthropomorphic phantom.
Objective.Adaptive radiotherapy (ART) requires robust end-to-end (E2E) testing tools capable of reproducing organ deformation, physiological motion, and multimodal imaging properties. TheToraco-abdominalAnthropomorphic Phantom withMotion forAdaptiveRadiotherapy (TAM-ARa) is a dynamic anthropomorphic phantom developed to support the design, validation, and quality assurance of online ART workflows.Approach.The phantom was constructed with anatomically realistic bone, lung, and abdominal organ models fabricated from tissue equivalent materials. Modular components allowed simulation of interfractional and intrafractional anatomical variations, including ventilator driven respiratory motion, abdominal deformation, and variable gastric filling. CT, CBCT, and 3T MRI scans were acquired to assess imaging performance. Two E2E tests of an online adaptive intensity-modulated radiotherapy (IMRT) workflow for liver tumors were performed on a Varian Ethos (ETHOS, Varian, USA) system. Multiple deformation scenarios were investigated, including a static reference configuration, two abdominal and one thoracic deformation for ionization chamber (IC) dosimetry, and a static plus abdominal deformation scenarios for radiochromic film dosimetry.Main results.The TAM-ARa phantom demonstrated realistic radiological characteristics, with CT Hounsfield units and MRI relaxation times closely matching reportedin vivovalues. The modular architecture allowed reproducible and controlled abdominal deformations, while setup and dosimeter placement were completed within minutes. In E2E tests for adaptive IMRT of liver tumors, excellent agreement was achieved between measured and planned dose distributions after the treatment plan was adapted. IC E2E measurements showed that online adaptation consistently restored accurate dose delivery under deformation, reducing deviations to below 3%. Independent film measurements confirmed sub-millimeter geometric accuracy and full planning target volume coverage after adaptation, demonstrating effective compensation of deformation-induced errors.Significance.The TAM-ARa phantom provides a versatile and reproducible platform for multimodal E2E testing and validation of online ART workflows. Employing its realistic anatomical design, modular structure, and motion capabilities, TAM-ARa was successfully applied for E2E tests to validate a workflow for adaptive IMRT of liver tumors.
8 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Preventive effects of dexamethasone oral solution on radiation-induced mucositis in patients with head and neck cancer: a randomized, triple-blind, parallel-group trial
PURPOSE: Radiation-induced oral mucositis is a frequent and debilitating complication of curative radiotherapy for head and neck cancer. This randomized, triple-blind, placebo-controlled, parallel-group trial evaluated whether a topical dexamethasone oral solution (0.5 mg/10 mL) used as a prophylactic mouthwash reduces the incidence and severity of radiation-induced oral mucositis. METHODS: Fifty-four patients with non-metastatic head and neck squamous cell carcinoma scheduled to receive ≥ 60 Gy radiotherapy were randomized 1:1 to dexamethasone mouthwash or placebo and instructed to gargle 10 mL four times daily during radiotherapy. The primary endpoint was the incidence and maximum severity of oral mucositis occurring during radiotherapy, defined as the highest grade recorded using the WHO Oral Toxicity Scale and NCI-CTCAE criteria v. 5. Secondary endpoints included patient-reported pain (VAS), dysphagia, treatment discontinuation, and mucositis status at 2 months. Fifty patients completed the study (25 per arm); four patients discontinued (reasons reported in Results). RESULTS: The dexamethasone group had significantly lower mucositis severity at all time points by WHO and CTCAE scales (all p < 0.01), a delayed peak severity (week 5 vs week 3), lower pain scores from weeks 4-7 (median VAS 0-3 versus 4-6; p < 0.01), and lower dysphagia at 2 months (32% vs 72%; p = 0.01). No grade 4 mucositis occurred in the dexamethasone arm; two grade 4 events occurred in placebo. There were no statistically significant differences in treatment discontinuation. Topical dexamethasone was well tolerated; no signal of treatment-related systemic steroid toxicity or increased oral infections was observed. CONCLUSION: In this phase II study, prophylactic topical dexamethasone mouthwash reduced the severity and delayed the onset of radiation-induced oral mucositis and improved patient-reported pain. These findings are promising but require confirmation in larger multicenter trials with longer follow-up and formal patient-reported outcomes (e.g., EORTC QLQ-H&N35, OMAS) and comparison with guideline-recommended strategies such as photobiomodulation. TRIAL REGISTRATION NUMBER: Iranian Registry of Clinical Trials; trial registration number: IRCT20240525061893N1; date of registration: 21 July 2024.
4 Apr 2026
Read appraisal →Annals of surgical oncology
A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer
BACKGROUND: Prostate cancer remains a leading cause of cancer mortality, yet current risk stratification systems inadequately integrate multidimensional tumor characteristics. This study aims to develop a deep learning-based multimodal prognostic model that combines genomic signatures, histomorphological features from whole-slide imaging (WSI), and clinical parameters to improve risk stratification and therapeutic decision-making. MATERIALS AND METHODS: We constructed a deep learning framework using two independent cohorts: The Cancer Genome Atlas (TCGA) for training and the Prostate, Lung, Colorectal and Ovarian (PLCO) trial for external validation. The model sequentially extracted 768-dimensional histopathological features from hematoxylin and eosin (H&E)-stained WSIs, predicted genomic scores, and histopathological scores, and integrated these with clinical variables via Cox regression to generate a multimodal prognostic score. Performance was evaluated through Kaplan-Meier analysis, Harrell's concordance index, and multivariate Cox regression. RESULTS: The prognostic score demonstrated superior prognostic accuracy (C-index: 0.774) compared with unimodal scores (genomic, histopathological, clinical) and NCCN risk stratification (C-index: 0.706-0.746, p < 0.05). High-risk patients identified by the multimodal model had significantly shorter progression-free intervals (HR = 9.088, 95% CI 6.033-13.691, p < 0.0001) and prostate cancer-specific mortality (HR = 8.787, 95% CI 3.238-23.847, p < 0.0001). Subgroup analysis within NCCN high-risk patients revealed distinct survival trajectories (p < 0.001). Gene set enrichment linked multimodal scores to tumor-relevant pathways. CONCLUSIONS: This integrative model eliminates reliance on genomic testing by computationally inferring genomic features from histopathology, while significantly enhancing prognostic precision. By stratifying heterogeneous patient populations and refining existing NCCN classifications, the prognostic score offers clinical potential for personalized risk assessment and optimized therapeutic strategies.
4 Apr 2026
Read appraisal →Japanese journal of radiology
Super-resolution deep learning reconstruction improves brain MRI quality and detection of metastases.
PURPOSE: Accurate identification of brain metastases is critical for determining prognosis and guiding treatment. Deep learning reconstruction (DLR) enhances MRI quality by reducing noise, while super-resolution DLR (SR-DLR) may further improve spatial resolution and lesion detectability. To evaluate SR-DLR versus conventional DLR in detecting and visualizing brain metastases on postcontrast T1-weighted brain MRI. MATERIALS AND METHODS: This retrospective study included 47 consecutive patients who underwent postcontrast 3D whole-brain T1-weighted MRI between July and December 2024. Images were reconstructed using both SR-DLR and DLR. Three independent readers evaluated metastatic lesion detection and rated image quality. Subjective assessments included lesion visibility, visibility of normal structures, sharpness, noise, and overall image quality. Objective metrics-full width at half maximum (FWHM), edge rise distance (ERD), edge rise slope (ERS), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)-were also measured. Statistical tests included jackknife alternative free-response receiver operating characteristic (JAFROC) analysis, Wilcoxon signed-rank test, McNemar's test, and paired t-tests, with significance threshold of p < 0.050. RESULTS: A total of 117 brain metastases were detected in 47 patients (mean age, 59 years ± 18; 27 men). SR-DLR demonstrated significantly better lesion detection performance than DLR (mean figure of merit = 0.842 vs. 0.797; p = 0.042). Subjective image quality ratings favored SR-DLR for lesion and structure visibility, sharpness, noise, and overall quality in most cases. Objectively, SR-DLR yielded lower FWHM (1.2 mm vs. 1.9 mm; p < 0.001), higher ERS (791.3 mm- 1 vs. 645.3 mm- 1; p = 0.013) indicating enhanced sharpness as well as improved CNR (27.5 vs. 24.9; p < 0.001) compared to DLR. CONCLUSION: Compared to DLR, SR-DLR significantly enhances brain MRI quality and improves detection of metastatic lesions.
4 Apr 2026
Read appraisal →Yonsei medical journal
Deep Learning for Automated Tumor Segmentation of Rectal Cancer on T2-Weighted Magnetic Resonance Images
PURPOSE: To develop a deep learning (DL)-based automated segmentation model for rectal cancer on T2-weighted (T2W) magnetic resonance (MR) images. MATERIALS AND METHODS: A total of 458 patients who underwent baseline rectal MR imaging were retrospectively enrolled. An experienced radiologist labeled the tumor on each slice of the selected T2W axial images covering the entire tumor mass, and tumor volume was measured during labeling. Another radiologist labeled the rectum on the same images. Attention U-Net was trained on the T2W images from the training dataset to classify each voxel as tumor or non-tumor. Segmentation with or without rectum guidance was compared using evaluation metrics, including the Dice similarity coefficient (DSC), in the test dataset. RESULTS: The tumor segmentation DSC without rectum guidance was 73.35% (71.97-74.71); that with the predicted rectum was 71.79% (70.53-73.05); and that with the overlaid rectum was 75.52% (74.32-76.62), which was the highest among the three segmentation models. Tumor volume showed a positive correlation with tumor segmentation accuracy with the predicted rectum (r=0.333, p=0.001; overlaid rectum, r=0.319, p=0.002) and without rectum guidance (r=0.219, p=0.036). CONCLUSION: A DL-based automated segmentation model can predict RC with over 70% accuracy, which was further improved with overlaid rectum, and larger tumor volume.
4 Apr 2026
Read appraisal →European radiology
Attention-based deep learning network for predicting World Health Organization meningioma grade and Ki-67 expression based on magnetic resonance imaging
OBJECTIVES: Preoperative assessment of World Health Organization (WHO) meningioma grading and Ki-67 expression is crucial for treatment strategies. We aimed to develop a fully automated attention-based deep learning network to predict WHO meningioma grading and Ki-67 expression. MATERIALS AND METHODS: This retrospective study included 952 meningioma patients, divided into training (n = 542), internal validation (n = 96), and external test sets (n = 314). For each task, clinical, radiomics, and deep learning models were compared. We used no-new-Unet (nn-Unet) models to construct the segmentation network, followed by four classification models using ResNet50 or Swin Transformer architectures with 2D or 2.5D input strategies. All deep learning models incorporated attention mechanisms. RESULTS: Both the segmentation and 2.5D classification models demonstrated robust performance on the external test set. The segmentation network achieved Dice coefficients of 0.98 (0.97-0.99) and 0.87 (0.83-0.91) for brain parenchyma and tumour segmentation. For predicting meningioma grade, the 2.5D ResNet50 achieved the highest area under the curve (AUC) of 0.90 (0.85-0.93), significantly outperforming the clinical (AUC = 0.77 [0.70-0.83], p < 0.001) and radiomics models (AUC = 0.80 [0.75-0.85], p < 0.001). For Ki-67 expression prediction, the 2.5D Swin Transformer achieved the highest AUC of 0.89 (0.85-0.93), outperforming both the clinical (AUC = 0.76 [0.71-0.81], p < 0.001) and radiomics models (AUC = 0.82 [0.77-0.86], p = 0.002). CONCLUSION: Our automated deep learning network demonstrated superior performance. This novel network could support more precise treatment planning for meningioma patients. KEY POINTS: Question Can artificial intelligence accurately assess meningioma WHO grade and Ki-67 expression from preoperative MRI to guide personalised treatment and follow-up strategies? Findings The attention-enhanced nn-Unet segmentation achieved high accuracy, while 2.5D deep learning models with attention mechanisms achieved accurate prediction of grades and Ki-67. Clinical relevance Our fully automated 2.5D deep learning model, enhanced with attention mechanisms, accurately predicts WHO grades and Ki-67 expression levels in meningiomas, offering a robust, objective, and non-invasive solution to support clinical diagnosis and optimise treatment planning.
4 Apr 2026
Read appraisal →Neuro Oncol
EANS-EANO guidelines on the extent of resection in gliomas
Intense efforts are employed to optimize neurosurgical resection of gliomas in different clinical scenarios. However, the level of evidence employed toward personalized treatment decisions is still moderate. Therefore, a joint task force was created by the European Association for Neurosurgical Societies (EANS) and the European Association of Neuro-Oncology (EANO) in order to provide interdisciplinary guidance on the extent of resection (EOR) in glioma surgery. This task force assessed the data on the EOR in gliomas, evaluated the level of evidence, and composed recommendations. The EOR is determined routinely by postoperative magnetic resonance imaging. Since EOR may be associated with survival and may have an impact on epilepsy, neurocognition, quality of life, neurological status, and also on planning of radiation and pharmacotherapy (evidence classes I-IV), recommendations of different levels can be made for the resection of newly diagnosed glioblastomas (level A), recurrent glioblastomas (level C), newly diagnosed (level B) and recurrent (good practice point) IDH mutant gliomas, ependymomas in children (level B) and adults (good practice point) as well as World Health Organization CNS grade 1 tumors (good practice point). We hereby report the outcome of this collaborative effort by the joint task force between EANS and EANO.
1 Jan 2026
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