Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 35 appraisals
European journal of radiology
A CT-based deep learning model to differentiate between benign and malignant adrenal lesions
OBJECTIVE: To develop a deep learning model to differentiate benign from malignant adrenal lesions. MATERIALS AND METHODS: A total of 380 patients with 385 pathologically confirmed adrenal lesions (101 malignant, 284 benign) were retrospectively included. Adrenal lesions were manually segmented on CT images and analyzed in a deep learning pipeline aimed at differentiating benign from malignant lesions. Four predictive models were developed that incorporated combinations of radiological data (tumor size, and spontaneous attenuation) and non-radiological data (i.e., medical history and laboratory results). Data of 267 patients were used as a training set and those of 113 patients for the test set. The diagnostic capabilities of the four models were estimated using sensitivity, specificity, accuracy, and areas under the receiver operating characteristic curves (AUC) using histopathological findings as the gold standard. The reproducibility of manual segmentation was estimated using the Dice similarity coefficient after blinded resegmentation of 40 adrenal lesions by an independent radiologist. RESULTS: Segmentation reproducibility achieved a mean Dice similarity coefficient of 0.92 ± 0.03 (range: 0.72-0.97). The most accurate model, which combined clinical, biological, and radiological data, achieved 84.2% accuracy (95% confidence interval: 79.9, 88.6) and an AUC of 0.93 (95% confidence interval: 89.9, 97.9) in the test set for diagnosis of malignant adrenal lesion. CONCLUSION: A deep learning model integrating preoperative clinical, biological, and radiological features demonstrates high capabilities in differentiating benign from malignant adrenal lesions on initial CT examination.
3 Aug 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis
BACKGROUND: Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete. METHOD: We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model. RESULTS: A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (18F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups. CONCLUSIONS: Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.
10 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 →The Journal of international medical research
Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis
ObjectiveThis systematic review and meta-analysis evaluates the diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in Chinese patients with lung adenocarcinoma.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (CRD420251273027), a systematic search of PubMed, Embase, Web of Science, the Cochrane Library, Scopus, China National Knowledge Infrastructure, Wanfang, VIP, and Chinese Biomedical Literature Database was conducted from inception to 31 October 2025. Two reviewers independently screened studies, extracted data, and assessed bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random-effects model was used to synthesize the data. Subgroup analyses were conducted for three factors: (a) imaging modality (computed tomography vs. positron emission tomography-computed tomography); (b) algorithm type (deep learning vs. conventional machine learning); and (3) validation strategy (external vs. internal).ResultsThirteen studies encompassing 6628 patients were included. The pooled sensitivity was 71% (95% confidence interval: 68-74), the pooled specificity was 81% (95% confidence interval: 78-84), and the summary area under the curve was 0.85 (95% confidence interval: 0.82-0.88). Deep learning models significantly outperformed conventional machine learning models (area under the curve: 0.871 vs. 0.798; P = 0.012). Computed tomography-based models yielded higher accuracy than positron emission tomography-computed tomography-based models (area under the curve: 0.879 vs. 0.828; P = 0.038). Models validated on independent external cohorts demonstrated superior performance compared with those relying solely on internal validation (area under the curve: 0.922 vs. 0.841; P = 0.006). Imaging modality was a significant source of heterogeneity (P < 0.05). No threshold effect or publication bias was detected.ConclusionMachine learning-based radiomics models exhibit promising diagnostic accuracy for the noninvasive prediction of epidermal growth factor receptor mutations in Chinese patients with lung adenocarcinoma. Computed tomography-based deep learning models subjected to independent external validation represent the current optimal approach. However, the retrospective nature and substantial heterogeneity of the included studies necessitate large-scale, prospective, multicenter trials with standardized workflows before clinical translation.
4 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
Scan-wise generalized PET denoising with contrastive adversarial learning
Objective.Deep learning has significantly advanced low-count positron emission tomography (PET) denoising. However, models trained on specific distributions often yield biased outputs when applied to scans with different activity distributions caused by anatomical and physiological variations (distribution shifts). Existing methods fail to generalize well across these scan-wise variations. Our goal is to formulate PET denoising as a scan-wise domain generalization (DG) problem to mitigate these variations and achieve robust, unbiased denoising for unseen scans.Approach.We propose a contrastive adversarial DG (CADG) framework to learn scan-invariant features efficiently. We leverage the property that multiple low-count noise realizations can be generated from a single raw list-mode PET scan to form a scan-wise domain distribution. Unlike conventional adversarial training with cross-entropy (CE) loss, we propose a contrastive adversarial framework that minimizes the mutual information between feature and scan-wise domains. Considering that each subject can have multiple scans in longitudinal studies, we further propose an ordered and memory-queued contrastive adversarial framework. This method efficiently includes realizations from the same scan as positive pairs, different scans of the same subject as pseudo-positive pairs, and different subjects as negative pairs in a memory batch. We explicitly exploit their ordered relationship as prior knowledge using a novel noisy-robust multi-positive ordinal contrastive loss.Main results.We systematically validated the effectiveness of our methods using 1920 noise realizations derived from 80 subjects with 192 longitudinal scans of MK-6240 tau PET data. The proposed CADG approach demonstrated superior denoising performance compared to CE-based adversarial methods and standard baselines. The ordered contrastive loss successfully improved the peak signal-to-noise ratio and structural similarity index consistently while reducing bias and standard deviation in Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-scan denoising from the perspective of DG. Our study is also a pioneering work in utilizing longitudinal scans as pseudo-positives within an ordered contrastive learning scheme to exploit fine-grained relationships for robust clinical PET imaging applications.
9 June 2026
Read appraisal →Blood advances
FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial.
This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission tomography/computed tomography ([18F]FDG-PET/CT) compared with conventional PET-derived features and biological/chromosomal abnormalities in patients with newly diagnosed multiple myeloma (NDMM) treated with daratumumab for induction/consolidation and/or maintenance and enrolled in CASSIOPET, a companion study of CASSIOPEIA, with long-term follow-up. Automated bone/liver CT-based segmentation were applied to the baseline [18F]FDG-PET images, with mTMTV being defined using the median liver background as the cut-off, including focal lesions and diffuse bone marrow (BM) involvement. Both univariate/multivariate Cox and machine learning (ML)-based survival models were performed. A total of 195 patients were included, 81% of them PET-positive. Multivariate analysis demonstrated independent prognostic value of mTMTV for progression-free survival (PFS) (P< .001) and overall survival (OS) (P< .001), complementary to Revised International Staging System (R-ISS) (P = .008 and P< .001, respectively). The ML model confirmed these findings, achieving concordance index of 0.609 and 0.659 and identifying mTMTV as the most informative feature for PFS and OS. Adding R-ISS, BM maximum standardized uptake value (SUVmax) and anemia to mTMTV accounted for >60% of the ML model explanation for PFS and adding R-ISS, the number of focal lesions and BM SUVmax for >60% of the model for OS. Combining R-ISS and mTMTV enabled the creation of 2 new-risk subgroups. In conclusion, this prospective study demonstrated the prognostic relevance of [18F]FDG-PET/CT-based parameters in the initial workup of patients with NDMM in the era of anti-CD38-based therapy. mTMTV was found to have strong independent prognostic value, complementary to R-ISS and refining risk stratification. This trial was registered at www.clinicaltrials.gov as #NCT02541383.
9 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 →Lancet (London, England)
Comparison of [18F]flortaucipir and [18F]MK6240 for the detection of tau pathology in Alzheimer's disease (HEAD): a multicentre, prospective, cross-sectional, within-participant study
BACKGROUND: Tau PET imaging has emerged as a critical biomarker for Alzheimer's disease, informing diagnosis, staging, and therapeutic selection. We investigated whether PET tracer selection alters tau detection. METHODS: We conducted a prospective, multicentre, non-randomised, within-participant comparison of [18F]flortaucipir (Tauvid), currently used in clinical settings in the USA and Europe, and [18F]MK6240, an investigational tau PET tracer. Participants were recruited from eight north American sites and underwent tau PET, amyloid-β (Aβ) PET, and detailed cognitive assessments. Tau PET with both agents was acquired within a 45-day window. Coprimary outcomes were the discriminative accuracy for Alzheimer's disease-related cognitive impairment and the frequency of tau positivity in early medial temporal lobe (MTL) and late neocortical regions. The study is registered with ClinicalTrials.gov, NCT05361382. FINDINGS: Between March 2, 2022, and Aug 27, 2025, 775 individuals were enrolled, with 682 completing all procedures (373 [55%] female, 309 [45%] male; 38 [6%] aged 19-27 years, 214 [31%] aged 50-65 years, and 430 [63%] aged 65-89 years). 32 (5%) participants identified as Hispanic or Latino. 637 (93%) identified as White, 24 (4%) as Black or African American, 16 (2%) as Asian, and five (1%) as other. In addition, 49 (7%) individuals were identified as being from a rural area. [18F]MK6240 showed greater accuracy than [18F]flortaucipir in distinguishing Alzheimer's disease from non-Alzheimer's disease impairment (area under the curve 0·93, 95% CI 0·89-0·95 vs 0·86, 0·75-0·91; p<0·0001). Among the older adults, tau positivity status was concordant in 560 (87%) for MTL and 603 (94%) for neocortical regions. In cognitively unimpaired participants, [18F]MK6240 identified twice as many MTL-positive cases as [18F]flortaucipir (n=54 [15%] vs n=23 [6%]). Prevalence ratio in Aβ-positive was 2·43 (95% CI 1·50-3·94; p=0·0003), identifying 23 additional cases per 100. Among discordant cases, 75 (89%) were [18F]MK6240-positive only and had higher Aβ burden (p<0·0001), APOEε4 frequency (p<0·0001), and cognitive impairment (p=0·0043) than those negative on both tracers. Neocortical tau positivity was more frequent with [18F]MK6240 than with [18F]flortaucipir in cognitively impaired individuals (80 [28%] vs 46 [16%]). Prevalence ratio in Aβ-positive was 1·74 (95% CI 1·32-2·29; p<0·0001), identifying 15 additional mild cognitive impairment and 21 dementia cases per 100. INTERPRETATION: Tau PET tracer selection influences the frequency of detection of tau pathology across the ageing and Alzheimer's disease spectrum. Compared with [18F]flortaucipir, [18F]MK6240 identified more individuals with tau pathology in cognitively unimpaired and cognitively impaired individuals, with direct implications for patient stratification in clinical trials and more precise guidance for therapeutic decision-making. FUNDING: National Institute on Aging.
31 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 →Current oncology reports
Impact of 18-FDG PET/CT on Multidisciplinary Decision-Making and Management in Abdominal Malignancies: A Systematic Review and Meta-Analysis
PURPOSE: To systematically review and assess the impact of 18 F-FDG PET/CT on multidisciplinary team (MDT) decision-making and management in adults with abdominal malignancies. MATERIALS & METHODS: We searched Ovid MEDLINE, Ovid Epub, Cochrane CENTRAL, EMBASE, and Scopus upto August 31, 2024, for studies including 18-FDG PET/CT and MDT management in abdominal malignancies in adult patients and describing pre- and post-PET/CT treatment plans or MDT decisions. Data extraction followed by independent two reviewer assessment using ROBINS-I framework. A random-effects meta-analysis of proportions was performed for studies reporting management change attributable to PET/CT. RESULTS: Fourteen studies met inclusion criteria, of which seven provided sufficient quantitative data for meta-analysis. A random-effects meta-analysis of the seven eligible quantitative studies showed that 21.8% of patients (95% CI: 10.6%-33.0%, p < 0.001; range: 8-36%) experienced a management change directly attributable to PET-based imaging. Between-study heterogeneity was substantial (I² ≈ 85%, p < 0.001). Reported management changes included cancellation or addition of planned surgery, modification of surgical extent, change in treatment intent between curative and palliative strategies, and alteration of systemic therapy or radiotherapy plans. Evidence on downstream outcomes such as survival, quality of life, and cost-effectiveness was limited and heterogeneous, precluding quantitative synthesis. CONCLUSION: PET/CT leads to a change in MDT management in roughly one in five patients with abdominal malignancies, often through re-staging and identification of occult metastases that alter treatment intent or surgical strategy. The magnitude of impact varies widely by clinical context and tumor type. 1. PET/CT identifies occult metastases and clarifies disease extent, allowing MDTs to avoid futile surgeries and appropriately shift between curative and palliative strategies. 2. Approximately one in five patients experience a meaningful change in management after PET/CT, demonstrating its significant influence on real-world treatment planning. 3. Incorporating PET/CT into MDT discussions enhances diagnostic confidence and supports more coordinated, evidence-based care across surgical, oncologic, and radiologic teams. By highlighting which patients benefit most, the findings support more efficient use of PET/CT, emphasizing the need for standardized outcome measures and prospective data to refine patient selection.
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 →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 →Journal of radiological protection : official journal of the Society for Radiological Protection
Fast occupational upper-limb radiation dose prediction using machine learning and Monte Carlo simulation
Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health risks. Existing extremity dosimeters such as thermoluminescents, optically stimulated luminescence rings and active personal dosimeters provide limited spatial information, exhibit angular and energy dependence and offer little or no real-time feedback. This study develops machine learning (ML) models to estimate radiation dose values at discrete upper-limb locations using Monte Carlo (MC)-derived data and procedure-specific parameters. A dataset of 10 000 MC dose maps was generated under varied clinical and geometric conditions. After log-transformation and normalisation, several ML models, including deep neural networks and tree-based regressor, were trained and assessed using five-fold cross-validation. Mean absolute error and relative error (RE) were evaluated on the original dose scale, and an ensemble of the three best-performing models was constructed to improve robustness. The ensemble consistently outperformed individual models, achieving an average RE of 3.69% and demonstrating stable performance across anatomical regions and dose levels. Highest accuracy was obtained for standard beam geometries, whereas larger discrepancies occurred in extreme configurations with steep dose gradients. Predicted dose patterns were consistent with the expected distributions across the upper-limb regions. The findings demonstrate the feasibility of ML-based extremity dose estimation in interventional environments. The proposed ensemble provides a rapid (around 10 ms), scalable alternative to full MC simulations, enabling near real-time predictions of upper-limb occupational dose and supporting optimisation of radiation protection practices in image-guided procedures.
16 May 2026
Read appraisal →Endocrine pathology
Impact of Molecular Testing on Surgical Decision-Making in Indeterminate Thyroid Nodules: A Global Meta-Analysis Across Test Generations
Thyroid nodules with indeterminate cytology represent a clinical challenge owing to uncertain malignancy risk, often leading to diagnostic surgery. Molecular testing has emerged as a promising adjunct to improve risk stratification and guide surgical decision-making. However, the clinical utility and impact of different molecular platforms remain debated. The present study evaluates the impact of molecular testing on surgical decision-making in indeterminate thyroid nodules and compares outcomes between first- and second-generation molecular platforms. A PRISMA-guided systematic review and meta-analysis identified 132 studies including 66,448 thyroid nodules, of which 30,292 (45.6%) were indeterminate (Bethesda III/IV). Pooled analyses included studies directly comparing conventional management with molecular testing or different molecular platforms. Primary outcomes were surgical resection rates and risk of malignancy (ROM), stratified by diagnostic approach and test generation. Conventional management demonstrated significantly higher surgical rates than molecular testing group (OR = 2.258, 95% CI: 1.548-3.293, p < 0.001). ROM among resected nodules was lower in the conventional management compared with the molecular testing group (OR = 0.594, 95% CI: 0.388-0.911, p = 0.017). First-generation molecular tests showed higher surgical rates and lower ROM than second-generation assays (OR = 1.709, 95% CI: 1.122-2.601, p = 0.013; OR = 0.665, 95% CI: 0.515-0.858, p = 0.002, respectively). Exclusion of non-invasive follicular thyroid neoplasm with papillary-like nuclear features attenuated differences in malignancy rates. Molecular testing significantly reduces surgical interventions in indeterminate thyroid nodules without evidence of reduced detection of clinically significant malignancy, particularly with second-generation platforms supporting more individualized and evidence-based management strategies.
14 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 →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 →Medical physics
Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT
BACKGROUND: The presence of scatter in computed tomography degrades image quality, and can be caused by the patient and by other components in the beam path, such as the bowtie filter. While conventional energy-integrating detectors do not provide spectral distinction, photon-counting (PC) detectors are energy-selective and provide spectral information about the incoming X-ray photons. Since each energy threshold is affected differently by scatter, this spectral information implicitly encodes the scatter content of a projection. PURPOSE: The purpose of this work is to investigate how the spectral information can be exploited to improve deep learning (DL)-based scatter correction. Furthermore, the performance of joint and separate patient and bowtie scatter correction will be investigated, addressing that bowtie scatter has not been considered in current DL-based approaches. METHODS: We present a DL-based approach that can estimate bowtie and patient scatter jointly and compare it against a separate correction. We also introduce neural network-based methods that incorporate the spectral information inherent in PCCT for scatter correction. We present networks that estimate scatter for up to four energy thresholds simultaneously. Training and validation was performed with Monte Carlo data as well as with real data measured by a clinical PCCT system. RESULTS: When comparing joint and separate patient and bowtie scatter estimation, both methods reduce the mean absolute error (MAE) from 8 HU to 1 HU. All proposed DSE methods effectively reduce scatter artifacts and perform better than the convolution-based reference approach. Incorporating the spectral information further improves the performance, with the DSE variant with four energy thresholds achieving the best overall results for all thresholds. For all energy thresholds tested, the spectral DSE methods reduced scatter errors originating from the patient and the bowtie in PCCT from up to 8 HU to below 1 HU. In addition to the global MAE, we report a critical MAE (MAE10) restricted to voxels with uncorrected errors > $>$ 10 HU, as such deviations are visually perceptible in soft tissue and exceed the noise level of modern CT systems. In all test cases, the proposed spectral methods reduced the MAE10 from 23.8 HU in the uncorrected images to 1.6 HU after spectral correction. The affected voxels comprised on average 25 % of the image volume, indicating a significant reduction in artifact intensity in the most affected areas. In virtual monoenergetic images (VMI), the application of spectral neural networks resulted in a significant reduction in MAE from ∼ $\sim$ 16 HU to ∼ $\sim$ 2 HU at 45 keV, from ∼ $\sim$ 8 HU to ∼ $\sim$ 1 HU at 70 keV, and from 5 HU to under 1 HU at 100 keV. CONCLUSIONS: This paper presents a combined method for correcting patient and bowtie scatter that delivers results equivalent to separate corrections and thus eliminates the need for multiple networks. Further, we demonstrate that deep scatter estimation can effectively exploit the spectral information available to improve scatter correction, especially for spectral applications, like VMIs. Spectral DSE networks slightly outperformed non-spectral variants, with multiple energy thresholds lead to more accurate estimations. This enables the use of one network for scatter correction and eliminates the need for multiple ones, thereby saving computational cost and complexity.
10 May 2026
Read appraisal →Journal of clinical ultrasound : JCU
Integrative Machine Learning Model Leveraging DCE-MRI and PSA Values for Advanced Risk Stratification in Prostate Cancer
PURPOSE: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learning (ML) model integrating Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) pharmacokinetic parameters with Prostate-Specific Antigen (PSA) values to predict ISUP grade metastatic risk groups. METHODS: This retrospective study included 102 patients with histologically confirmed prostate cancer. DCE-MRI pharmacokinetic parameters (Ktrans, Kep, Ve, CER, MaxSlope, IAUGC) were standardized. The dataset was balanced using the Synthetic Minority Oversampling Technique and split into training, validation, and test sets. ML models, including Random Forest, were evaluated using Area Under the Curve (AUC) values. RESULTS: The Random Forest classifier achieved the highest performance, with an AUC of 0.92. Precision-recall analysis identified an optimal threshold of 0.3, balancing sensitivity and specificity for high-risk group detection. SHAP analysis highlighted PSA, MaxSlope, and Kep as key predictors contributing to model accuracy. CONCLUSION: Integrating DCE-MRI parameters with PSA values using ML algorithms enhances the prediction of ISUP grade metastatic risk groups. This method provides a robust tool for metastasis screening and personalized treatment in prostate cancer.
9 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 →Radiology. Imaging cancer
ONCO-RADS-guided Large Language Models for Extraction and Classification of Incidental Findings on Whole-Body Imaging Reports
Purpose To evaluate large language model (LLM)-based strategy performance for extraction and classification of incidental findings from whole-body (WB) imaging reports, particularly strategies incorporating Oncologically Relevant Findings Reporting and Data System (ONCO-RADS). Materials and Methods In this retrospective bicenter study, authors included all WB MRI reports from January 2016 to December 2023 at a referral center (internal dataset). Two observers extracted all incidental findings, and patient records were used to confirm final diagnoses. First, authors evaluated ONCO-RADS performance and the reproducibility of its incidental finding classifications by six radiologists. Then, authors evaluated the accuracy of three LLM-based strategies: (a) a fine-tuned DeBERTa/medical named entity recognition (NER) model; (b) zero-shot LLMs (ChatGPT-o1 [OpenAI], Gemini-2.5-Pro [Google]); and (c) reference-guided prompting of these LLMs using ONCO-RADS. Authors then expanded these strategies to an external dataset of 605 reports with multiple imaging techniques (405 WB MRI; 100 fluorodeoxyglucose PET/CT; and 100 chest-abdomen-pelvis CT acquisitions) from January 2022 to January 2025. Results The internal dataset included 823 patients (mean age, 63.7 years ± 11.7 [SD]; 457 male patients) with 1488 WB MRI reports. The average interobserver reproducibility of ONCO-RADS incidental finding classifications was excellent (Cohen κ, 0.87). The per-report accuracies of ONCO-RADS-guided LLMs (95.6% [151 of 158] and 86.7% [137 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) were higher than those of the medical NER (69.0% [109 of 158]) and zero-shot LLMs (57.0% [90 of 158] and 70.9% [112 of 158] for ChatGPT-o1 and Gemini-2.5-Pro, respectively) (P < .001). In the external test set (mean age, 60.6 years ± 12.9; 330 male patients), the per-report accuracies of ONCO-RADS-guided ChatGPT-o1 (83.5% [505 of 605]) and Gemini-2.5-Pro (82.0% [496 of 605]) were higher than those of the models without ONCO-RADS prompting (63.1% [382 of 605] and 61.2% [370 of 605], respectively) and the medical NER (55.7% [337 of 605]) (P < .001). Conclusion Reference-guided prompting of the LLMs ChatGPT-o1 and Gemini-2.5-Pro with ONCO-RADS improved their performance in extracting and classifying incidental findings on WB imaging reports compared with zero-shot prompting and medical NER. Keywords: Large Language Models, Incidental Findings, Whole-Body MRI Supplemental material is available for this article. © RSNA, 2026.
6 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 →European journal of nuclear medicine and molecular imaging
Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients.
PURPOSE: Selective Internal Radiotherapy (SIRT) is an established treatment option for hepatocellular carcinoma (HCC). However, a major complication is radioembolization-induced liver disease (REILD). METHODS: This retrospective study, analyzed patients treated with SIRT for HCC to identify clinical factors associated with REILD and to predict treatment response. Machine learning (ML) methods were applied to two distinct cohorts to determine predictors of toxicity and response. RESULTS: Among 138 patients analyzed for REILD, ML identified bilirubin as a key predictor. A refined threshold of 26.5 µmol/L (1.55 mg/dL) was associated with toxicity risk. In the response cohort (136 patients), predictive performance was limited, nevertheless tumor dose appeared as the most frequent feature selected by the models. CONCLUSIONS: Bilirubin was confirmed as a critical factor for REILD prediction with the identification of a new threshold that may improve patient risk stratification. While tumor dose appears as the main predictor of treatment outcome, robust response models require additional features.
5 May 2026
Read appraisal →European radiology
A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
OBJECTIVES: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. MATERIALS AND METHODS: In this multicenter retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization. RESULTS: 203 MRI studies from 72 VS patients (mean age, 58.51 ± 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 ± 0.113 to 0.993 ± 0.009, and the peak signal-to-noise ratio increased from 21.6 ± 3.73 dB to 41.4 ± 4.84 dB. At a 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent image quality (3, interquartile range (IQR) [Q3-Q1] = 3-3 and 3, IQR [Q3-Q1] = 4-3), with the latter being considered more informative (4, IQR [Q3-Q1] = 4-3). CONCLUSION: The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10-30% of the standard dose. KEY POINTS: Question Deep learning models that aid in the reduction of contrast agent dose are not extensively evaluated for MRI of the cerebellopontine angle cistern. Findings Deep learning models restored the low-dose MRI of the cerebellopontine angle cistern, yielding images sufficient for vestibular schwannoma diagnosis and management. Clinical relevance Deep learning models make it possible to reduce the use of gadolinium-based contrast agents for contrast-enhanced MRI of the cerebellopontine angle cistern.
4 May 2026
Read appraisal →Physics in medicine and biology
Few shots transfer learning for universal SPECT denoising across diverse acquisition protocols
Objective.Accelerated single photon emission computed tomography (SPECT) imaging, achieved by reducing either the number of projection angles or the acquisition time per angle, enhances clinical workflow efficiency but introduces elevated noise. This study aims to develop and validate a universal DL-based reconstruction framework that effectively generalizes across diverse, clinically-realistic SPECT acceleration protocols by overcoming the data scarcity challenge.Approach.SPECT bone scans from 103 patients were acquired under a standard scan (60 views, 12 s/view, 60v12s) followed by a fast scan using one of five acceleration protocols (60v6s, 60v3s, 30v12s, 30v6s, 16v12s). AU-Net-based reconstruction framework was implemented using three strategies: (1) single model, trained on individual acceleration protocol), (2) base model, trained on aggregated datasets from five acceleration protocols, and (3) transfer model, fine-tuned from the base model for protocol-specific optimization. Pixel-level accuracy and structural similarity were assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) metrics, and maximum pixel value of lesions. Clinical evaluation of image quality, radionuclide detail, artifacts, and diagnostic confidence was conducted using a 5-point system.Main results.Quantitative evaluation showed the transfer model achieved better PSNR and SSIM across all protocols (highest 48.02 PSNR and 0.9918 SSIM in 30v6 s protocol). Qualitative analysis confirmed enhanced structural fidelity. Clinical evaluations rated the transfer model highest across metrics, with scores of 4.667 ± 0.508 (image quality, 30v12 s), 4.800 ± 0.250 (radionuclide detail, 60v6 s), 1.150 ± 0.173 (artifact reduction, 60v6 s), and 4.800 ± 0.250 (diagnostic confidence, 60v6 s), surpassing full-scan results in most cases.Significance. The proposed transfer learning (TL) framework effectively addressed data scarcity and improved reconstruction performance across diverse SPECT acceleration scenarios. The adoption of a TL strategy mitigates data scarcity by utilizing shared features and fine-tuning for specific protocols. The framework demonstrates potential for integration into fast SPECT workflows, facilitating reliable use across diverse imaging scenarios.
2 May 2026
Read appraisal →Physics in medicine and biology
Confounding factors in the generalisation of synthetic CT to diagnostic spine MRI
Objective. Generalisation of synthetic CT (sCT) generation to diagnostic MRI data in the spine faces many challenges, particularly when aiming at accurate visualisation of pathologies for orthopaedic treatment management. In this study we assessed the effect of potential confounding factors on the performance of sCT generation from diagnostic MR images.Approach. Paired spinal diagnostic MR and CT scans from two centres (51 patients) were collected retrospectively, spanning multiple spinal pathologies and regions. Each patient's dataset contained 3D T1-/T2-weighted and 2D T1- and T2-weighted scans. 3D U-Nets were trained per centre on the 3D MR data and CT using a mean absolute error (MAE) loss. The performance was assessed on sCTs derived from original 2D and 3D data and from simulated 2D data with varying orientation (axial/coronal/sagittal) and slice spacing (1.1-6.6 mm). The sCTs were compared to the CTs using MAE, peak signal-to-noise ratio, and dice similarity coefficient.Main results. We identified MR resolution, orientation and contrast as confounding factors for sCT generation from diagnostic MR. Additionally, CT noise and CT-to-MR registration were identified to influence the performance evaluation. Performance degraded with larger slice spacings and significant differences between orientations occurred more often at larger slice spacings. The effect of slice spacing and orientation was stronger inside the vertebrae than outside. While the networks performed better overall on contrasts more similar to the training contrasts, they demonstrated promising performance when trained on 3D T2w images and tested on 2D T1w images. Finally, higher CT noise levels resulted in worse sCT performance metrics, reflected in a significant correlation between noise and MAE.Significance. This work demonstrates that generalisation of sCT generation to diagnostic spine MRI data is hampered primarily by MRI acquisition related aspects including MR resolution, orientation and contrast, which provides guidance for future work on generalisable sCT from diagnostic MR.
27 Apr 2026
Read appraisal →Physics in medicine and biology
MRDT-GAN: generative adversarial network with multi-scale residual dense transformer generator for low-dose CT denoising
Objective.Low-dose computed tomography (LDCT) reduces radiation exposure but introduces noise and artifacts that degrade diagnostic quality. Existing deep learning-based denoising methods still face challenges such as over-smoothing, loss of fine structures, and uneven contrast. This study aims to develop an LDCT denoising framework that enhances noise suppression while preserving anatomical details and structural fidelity.Approach.We propose a multi-scale residual dense transformer generative adversarial network (MRDT-GAN). In the generator, we adopt the MRDT Block as the core unit, which introduces multi-scale strategy into residual dense network to reduce over-smoothing and preserve fine details, and also patching transformer block to capture long-range dependencies, mitigating distortions caused by localized receptive fields in convolutional neural network-based approaches. A hybrid attention module is also introduced in the generator to process spatial, frequency, and contrast information, enabling the network to focus on critical regions for noise suppression, improve contrast uniformity, and maintain texture consistency. In the discriminator, we adversarially explore differences on global, pixel, and also sub-scale between denoised LDCT and normal dose CT to better capture structural variations, reduce local noise and distortions, and ensure more realistic texture reconstruction while minimizing artifacts.Main results.We validate MRDT-GAN on both the NIH-AAPM-Mayo Clinic LDCT dataset and a real-world dataset. Experimental results indicate that MRDT-GAN achieves superior denoising performance compared with existing methods, effectively preserves details, enhances visual quality, and achieves a better balance between noise suppression and structural integrity.Significance.MRDT-GAN provides an effective and generalizable LDCT denoising solution that balances noise reduction with fine-detail preservation. By integrating MRDT modeling, hybrid attention mechanisms, and multi-difference adversarial learning, the framework offers improved clinical applicability and supports high-quality image reconstruction for downstream diagnostic tasks.
27 Apr 2026
Read appraisal →The Journal of clinical endocrinology and metabolism
An Interpretable Machine-Learning Model for Predicting Occult Central Lymph Node Metastasis in Papillary Thyroid Cancer
CONTEXT: Accurate preoperative prediction of occult lymph node metastasis (OLNM) in clinically lymph node negative (cN0) papillary thyroid carcinoma (PTC) is critical for optimizing therapeutic strategy, particularly for thermal ablation and active surveillance. OBJECTIVE: The aim of this study was to develop an interpretable machine-learning (ML) model to predict the risk of OLNM in cN0 PTC patients. METHODS: This retrospective study analyzed data of 961 cN0 PTC patients (August 2018-August 2023). Multivariable logistic regression identified independent risk factors for OLNM in cN0 PTC. The cohort was randomly divided into the training and test sets, and a subset of patients with tumors sized 1 cm or less was further extracted from the test set for internal validation. Eight ML models incorporating clinical, ultrasonographic, and molecular features were developed and evaluated. Shapley Additive exPlanations (SHAP) enhanced interpretability. RESULTS: RET fusion positivity and BRAF mutation positivity were identified as independent molecular risk factors for OLNM in cN0 PTC, alongside 6 clinical and ultrasonographic variables. Nine predictors were incorporated into the predictive model. The random forest (RF) model achieved optimal performance with an area under the curve (AUC) of 0.906 in the training set and 0.733 in the test set, along with the lowest Brier scores of 0.135 and 0.212, respectively. Analysis of tumors sized 1 cm or less internally validated the model's robustness with an AUC of 0.719. SHAP analysis identified size, age, and clustered punctate echogenic foci as the top predictors. CONCLUSION: This is the first study to identify RET fusion positivity as an independent OLNM risk factor in cN0 PTC. The developed RF model demonstrates moderate predictive performance for OLNM risk and provides a framework for integrating clinical, sonographic, and molecular data, and is deployed as a web calculator (https://predictingoccultlymphnodemetastasis.shinyapps.io/web3/).
23 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
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 →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 →European thyroid journal
Promise and pitfalls of AI chatbots in complex decision-making for thyroid nodules and papillary thyroid cancer
INTRODUCTION: Artificial intelligence (AI) chatbots are increasingly used in medicine, but their reliability in scenarios with multiple management options is unclear. Indeterminate thyroid nodules and low- and low-to-intermediate-risk papillary thyroid carcinoma (PTC) represent such cases. METHODS: In a nationwide web-based survey, 201 members of the Hellenic Endocrine Society evaluated 12 clinical vignettes on indeterminate thyroid nodules and low- and low-to-intermediate-risk PTC. Their responses were compared with those generated by four conversational AI models (ChatGPT, Gemini, Copilot, and DeepSeek) at two time points, 11 months apart. DeepSeek was assessed only at the second time point. Chatbot outputs were assessed for agreement with endocrinologists' predominant answers, concordance with the most guideline-consistent options (American and European Thyroid Association recommendations), temporal stability, and inter-model agreement. RESULTS: Alignment between chatbots and endocrinologists' predominant responses was limited, reaching at most 25% across scenarios. In contrast, concordance with the most guideline-consistent options was higher, up to 83% (10/12 scenarios), depending on the model and time point. Across 12 scenarios, ChatGPT, Gemini, and Copilot changed their responses in 4, 7, and 5 scenarios, respectively, with some updates moving closer to, and others further from, guideline-based answers. Inter-model agreement ranged from 33 to 67%, indicating substantial variability among chatbots. CONCLUSION: AI chatbots show evolving but inconsistent performance in complex thyroid management scenarios. While guideline concordance can be relatively high, substantial variability across models, limited temporal reproducibility, and poor alignment with clinical practice highlight the need for ongoing longitudinal evaluation before safe integration into clinical decision-making.
9 Apr 2026
Read appraisal →PloS one
Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma
RATIONALE AND OBJECTIVE: The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan and prognosis. This study aims to develop and validate the application value of Vision transformer (ViT) model in preoperatively predicting cervical LNM in PTC. MATERIALS AND METHODS: A total of 540 PTC patients were retrospectively reviewed from two hospitals from April 20,2022 to August 20,2023.The ViT model is built based on the two-dimensional rectangular ultrasound image of the primary thyroid tumor, and at the same time, to compare its performance, a deep learning model of the traditional Convolutional neural network (CNN) framework, a ultrasound radiomics combined model(Clinical-Rad model), and clinical model are built. RESULTS: The ViT model demonstrated an AUC of 0.807 (95% CI: 0.709-0.905) in the internal validation cohort and 0.809 (95% CI: 0.720-0.900) in the external validation cohort. The ViT model's AUC ranged from 0.807-0.814 across all cohorts, significantly exceeding the clinical model (AUC: 0.595-0.669, P<0.001). While the AUC of the ViT model in the training cohort was slightly lower than that of the combined ultrasound radiomics model (0.814 vs 0.828, P=0.491), it showed significantly higher AUC values in the internal (0.807 vs 0.718, P=0.049) and external validation cohorts (0.809 vs 0.691, P<0.001). Compared to the clinical and combined radiomics models, the ViT model exhibited stable and superior predictive performance for PTC cervical lymph node metastasis.In the internal validation cohort, Doctor C's net reclassification improvement (NRI) with the ViT model was 0.106 (P=0.022), and the integrated discrimination improvement (IDI) was 0.106 (P=0.023). Doctor D showed NRI and IDI values of 0.113 (P=0.022) and 0.106 (P=0.024), respectively. In the external validation cohort, Doctor C's NRI and IDI were 0.090 (P=0.024) and 0.106 (P=0.024), while Doctor D had values of 0.011 (P=0.013) and 0.106 (P=0.013). The ViT model enhanced the diagnostic capabilities of both Doctor C, with less clinical experience, and Doctor D, with extensive experience. CONCLUSION: The deep learning model based on the Transformer mechanism shows good performance in predicting LNM in PTC patients, which is superior to the clinical model, Clinical-Rad model, and traditional CNN model.
5 Apr 2026
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