Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 42 appraisals
Neurosurgical review
Development and external validation of a machine learning model for predicting postoperative hydrocephalus in 1,073 posterior fossa tumor patients
Postoperative hydrocephalus is a common complication following posterior fossa tumor resection, affecting 7-40% of patients. Although preoperative cerebrospinal fluid (CSF) diversion may be required in selected patients with hydrocephalus, decisions remain individualized in routine neurosurgical practice. We therefore developed and externally validated a model to provide supplementary preoperative risk stratification using routinely available variables. We retrospectively analyzed 1,073 patients following resection of posterior fossa tumors (PFTs) treated at five tertiary centers between 2013 and 2024, dividing them into a development cohort (n = 854) and an external validation cohort (n = 219). We initially screened 30 perioperative variables from the multicenter dataset and then selected a clinically implementable model restricted to variables available before tumor resection. Feature importance was ranked using Shapley additive explanations (SHAP), and seven distinct machine learning (ML) algorithms were evaluated. Model performance was comprehensively measured using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision-recall curves, and decision curve analysis. The final clinically implementable model included three preoperative variables: Evans index, tumor-fourth ventricle relationship, and preoperative cerebrospinal fluid diversion status. In the external validation cohort, the support vector machine model showed good external discrimination, with an area under the receiver operating characteristic curve of 0.877, accuracy of 81.3%, sensitivity of 80.8%, and specificity of 81.7%. Logistic regression achieved comparable discrimination. Calibration assessment suggested dataset shift between the development and external validation cohorts, indicating that absolute predicted probabilities should be interpreted cautiously in populations with different baseline risks. A concise three-variable preoperative model showed good external discrimination for clinically relevant postoperative hydrocephalus after posterior fossa tumor resection. The model may support preoperative risk communication and postoperative surveillance planning, but should not be used as a stand-alone indication for cerebrospinal fluid diversion. Prospective validation and local recalibration are warranted before routine clinical implementation.
31 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Patient experiences of tissue donation and digital consent support in primary craniospinal tumour research.
PURPOSE: Requests for tissue donation for research are often made at times of heightened vulnerability, particularly around diagnosis and surgery. This study explored patient experiences of tissue donation discussions, perspectives on consent, and the acceptability of digital decision support in primary craniospinal tumour research. METHODS: A UK national online cross-sectional survey was conducted with 50 adults with a primary brain tumour or spinal sarcoma. The survey was developed with patient and public involvement; six patient contributors reviewed the initial questionnaire before launch. Descriptive statistics summarised closed responses, and open-text comments were grouped descriptively to contextualise quantitative findings. Reporting was informed by STROBE guidance. RESULTS: Just over half of participants reported being invited to donate tissue for research (26/50, 52%). Respondents strongly preferred tissue donation to be discussed at or after a clinic appointment, and none selected the day of surgery as the preferred time. Among invited respondents, most reported that information was easy to understand (22/26, 85%), that they had an opportunity to ask questions (23/25, 92%), and that they had sufficient time to consider the decision (23/26, 88%). Sixteen of 26 invited respondents (62%) discussed the decision with family or friends; among invited respondents who had not done so, 7/10 (70%) would have liked the opportunity. Interest in a secure digital adjunct was high (46/49, 94%). CONCLUSION: Overall experience was generally positive, but the data identify specific, practical opportunities to strengthen consent support in rare craniospinal tumour pathways, including appropriate timing, clear and revisitable information, opportunities for question-asking, and resources that support family-inclusive decision-making.
19 July 2026
Read appraisal →NeuroImage
Rapid multi-parametric quantitative MRI via deep learning-based synthetic-to-real reconstruction and 3D SSFP-MOLED imaging
Multi-parametric quantitative magnetic resonance imaging (mqMRI) holds significant clinical potential through multi-parametric tissue characterization, yet its adoption is hindered by prolonged scan time and sensitivity to non-ideal signal conditions, especially in high-resolution whole-brain protocols. To address these challenges, we propose a novel signal encoding method integrating phase-modulated three dimensional steady-state free precession with multiple overlapping-echo detachment (3D SSFP-MOLED). This method simultaneously encodes six physiological parameters (M0, T1, T2, T2*, B1+, ΔB0) into k-space by controlling overlapping echo detachment in signal acquisition. A physics-constrained synthetic data pipeline was developed to simulate MR signal evolutions with realistic field variations (ΔB0, B1+ inhomogeneities), enabling robust training of network for real-time parameter mapping. Whole-brain parametric maps (1×1×2 mm³ resolution) can be delivered within 3 minutes with only 2x parallel acquisition acceleration. Validation was performed on phantom, healthy volunteers, and clinical cases with tumors/hemorrhage. Experimental results show that our method can achieve rapid multi-parametric quantitation with high accuracy and reproducibility. By synergizing adaptive signal encoding, physics-informed synthetic training, and reproducible deep learning reconstruction, this work establishes a new paradigm for efficient and reliable mqMRI in clinical signal processing applications.
17 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 →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 →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 →PloS one
Deep learning based two-way feature depiction model for brain tumor detection
Brain tumors are one of the most fatal disorders that cause one of the highest mortalities in the world. Gliomas are the most common primary brain tumors originating from glial cells in the central nervous system. Traditionally, a tissue sample is extracted and examined for its genetic and characteristic properties. This method is invasive, painful, and takes a longer period to produce results. Various automatic Deep learning (DL) based schemes have been presented for the brain glioma detection, but they lack due to poor explainability, lower generalization, poor feature depiction, class imbalance problem and lower detection rate. This paper presents a deep learning based brain tumor detection using two way feature depiction model (TWFDM) that combines the 2D-Deep Convolution Neural Network (DCNN) and 1D-DCNN. The 2D-DCNN accepts the raw MRI images and the 1D-DCNN accepts the handcrafted local binary pattern (LBP), gray level cooccurrence matrix (GLCM), and Histogram of Oriented Gradient (HOG) features. Furthermore, improved particle swarm optimization (IPSO) is used for feature selection to minimize the computational complexity of the TWFDM system. The proposed TWFDM achieves an overall accuracy of 96.25%, a recall of 96.34%, a precision of 96.31%, and an F1-score of 96.32% on the Brain MRI dataset for four-class classification, representing an important improvement over traditional techniques.
3 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 →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 →Progress in biomedical engineering (Bristol, England)
Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.
Gliomas are heterogeneous primary central nervous system tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in histopathology is underway as samples are digitized into whole-slide images (WSIs). Deep learning (DL) shows great potential for glioma diagnosis, subtyping, grading, and prognosis. This review summarizes recent advances in WSI-based DL models, covering data preprocessing, model architectures, and translational performance. Model evolution has progressed from CNNs (e.g. ResNet, capturing local features via convolution) to Transformers (e.g. Vision transformer, modeling global dependencies via self-attention), hybrid architectures, and large language model. Typical pipelines include quality control, stain normalization, patch extraction, feature integration, and prediction. Public datasets such as The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium serve as key resources. On various datasets (public and institutional), a glioma-specific model ResNet-50 achieved AUC 0.983 for subtype classification; a Vision Transformer model reached AUC 0.960 for molecular typing; a hybrid model, ROAM, attained AUC 0.990; and the pan-cancer hybrid model called CHIEF achieved AUC 0.9397 for diagnosis. Attention heatmaps and Shapley Additive exPlanations provide interpretability by linking model outputs to histologic regions. Novel multimodal fusion integrates genomic, proteomic, radiologic, and clinical data to enhance prediction and uncover biological relevance. Future research may focus on enhancing model generalizability and predictive accuracy via advanced architectures, developing lightweight models for resource-limited settings to facilitate clinical translation.
20 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 →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 →Gene
Utilization of next generation sequencing from glioma patient liquid biopsy to determine survival prognostics: A systematic review
Glioma is a malignant cancer that affects the central nervous system. Early detection of glioma is still difficult due to the hard-to-reach location of the cancer. The use of next generation sequencing is one of the new developments that can be used for diagnosis, however the use of this modality for prognosis is still rarely discussed. This systematic review aims to determine the benefits of using next generation sequencing (NGS) on the assessment of ctDNA or cfDNA concentration, progression free survival (PFS), and overall survival (OS). This systematic review was made using data from six databases, namely PubMed, PMC, ProQuest, Google Schoolar, ScienceDirect, and SCOPUS, and was compiled based on guidelines from PRISMA. Fourteen studies were included in this systematic review with a total of 663 glioma patients. The results of ctDNA measurements generated through the NGS method with liquid biopsy samples show that the higher the grading and sampling when the tumor progresses can increase the ctDNA value. When assessing ctDNA concentrations at baseline when samples were taken, most studies showed a worse prognosis for PFS and OS survival time. However, the grouping of patients in the current studies is still highly variable so further studies with standardized ctDNA cut off values are needed for more precise determination of prognosis.
7 June 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
Predicting progression-free survival in glioblastoma with neuroimaging and machine learning
PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. METHODS: GBM patients (N = 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training. RESULTS: Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p < 0.001). CONCLUSIONS: Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes.
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 →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 →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 →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 →Neurosurgical review
The role of preoperative thrombocytic factors on survival in patients with glioblastoma: a meta-analysis and synopsis of the literature
Glioblastoma (GBM) is the most common malignant primary brain tumour and is associated with poor prognosis. Platelet-related factors have been linked to outcomes in GBM, but evidence remains inconsistent. This review evaluates their prognostic value for survival. We conducted a systematic review and meta-analysis of studies on preoperative thrombocytic factors in GBM. PubMed, MEDLINE, and Embase were searched from inception to January 2025 using MeSH terms. Eligible studies included observational cohort studies of adults (≥ 16 years) with WHO grade 4 diffuse astrocytoma (GBM), ≥ 30 participants, and ≥ 3 months' follow-up after resection. Random-effects meta-analysis using restricted maximum likelihood with Hartung-Knapp adjustment was used to pool hazard ratios (HRs) of high vs. low parameters on overall survival (OS). Study quality was assessed with the Newcastle-Ottawa Scale. 21 studies were included. 13 (n = 2609) reported HRs of high vs. low platelet: lymphocyte ratio (PLR, median threshold = 150), with high PLR significantly associated with worse OS (pooled HR = 1.46, 95% CI [1.23, 1.74]). Eight studies (n = 1921) assessed platelet count (PC, median threshold = 208*109/L), showing high PC was a borderline predictor of worse OS (pooled HR = 1.38, 95% CI [1.00, 1.90]). Four studies (n = 1234) examined mean platelet volume (MPV, median cut-off = 9.05 fL), which qualitatively demonstrated worse OS in patients with high MPV or high MPV: PC ratio. One study assessed platelet distribution width (PDW, threshold = 14.7 fL), finding worse OS in the high PDW group. High preoperative PLR is associated with poor prognosis in GBM, while high PC demonstrates borderline increased risk, which may help refine risk stratification and guide future mechanistic studies.
25 May 2026
Read appraisal →European radiology experimental
MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness
OBJECTIVES: To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contrast-enhanced (CE)-T1-weighted images, individually and combined, to predict the invasiveness of pituitary neuroendocrine tumors (PitNETs). MATERIALS AND METHODS: Patients with macro-PitNETs were retrospectively enrolled from 2019 to 2022. Radiomic features were extracted from manually segmented lesions on preoperative T2-weighted and CE-T1-weighted images and, after a feature selection step, used to assess invasiveness, defined following Trouillas' classification. Five machine learning models (logistic regression, random forest, gradient boosting, AdaBoost, XGBoost) were trained using CE-T1-weighted, T2-weighted, and CE-T1-weighted plus T2-weighted features. Performance was evaluated on a test set using the area under the receiver operating characteristic curve (AUC). RESULTS: Two hundred patients were included in the study: 95 PitNETs were noninvasive (74 grade 1a; 21 grade 1b) and 105 invasive (70 grade 2a; 35 grade 2b). A total of 102 radiomic features were extracted per sequence. The best-performing model was the XGBoost, using five combined CE-T1-weighted and T2-weighted features, with an AUC of 0.85 (95% confidence interval: 0.75‒0.95). Lower AUC values were obtained for logistic regression using CE-T1-weighted images (0.80) and AdaBoost using T2-weighted images (0.78). CONCLUSION: The XGBoost model, incorporating tumor shape, texture, and first-order features extracted from both CE-T1-weighted and T2-weighted MRI, showed high performance in predicting PitNETs invasiveness. This radiomic model might help identify tumors with a higher risk of disease persistence, recurrence, or progression. RELEVANCE STATEMENT: The radiomic model based on contrast-enhanced T1-weighted and T2-weighted MRI demonstrated high discriminative ability in predicting invasiveness of pituitary neuroendocrine tumors and could aid in identifying tumors that may be at higher risk for recurrence or progression, ultimately improving patient outcomes through personalized treatment strategies. KEY POINTS: Pituitary neuroendocrine tumors (PitNETs) represent a significant challenge in clinical practice. Accurate preoperative prediction of PitNET invasiveness is crucial for surgery and prognosis. Contrast-enhanced T1-weighted and T2-weighted MRI-based radiomic model effectively predicts PitNET invasiveness. The developed radiomic model could help optimize individualized treatment decisions before surgery.
21 May 2026
Read appraisal →PloS one
A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation
Accurate brain tumor segmentation by multi-modal MRI is crucial for diagnosis, treatment planning, and prognostic assessment. This work proposes a novel hybrid architecture within a GA optimized by 3D residual U-Net (ResUNet) with generative adversarial network (GAN) for the accurate segmentation of tumor subregions, namely the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), using a dataset collected from BraTS2023 (437 training and 188 validation). By using GAs, we enable the computer to automatically find the best model structure and training settings, which helps keep the model simple enough to avoid overfitting while being powerful enough to generalise well to new data. The GAN part of our method trains the model to draw more realistic and consistent boundaries around tumor regions in MRI scans, thereby improving the overall quality of the segmentation. In our experiments, this GA-ResUNetGAN approach performed better than several leading methods, such as 3D UNet, DynUNet, UNETR, SwinUNETR, and the standard ResUNet, especially in accurately identifying TC, WT, and ET parts-achieving Dice scores of 0.94, 0.96, and 0.91 on the validation set. In addition, K-Fold cross-validation was performed to ensure consistent performance across different data splits. While the method involves more processing work than some baseline techniques, it provides improved segmentation quality and boundary delineation on the evaluated dataset. These results demonstrate the advantages of combining evolutionary optimization with adversarial deep learning for challenging medical imaging tasks.
19 May 2026
Read appraisal →Journal of neuro-oncology
Should checkpoint inhibitors be reserved for biomarker-selected pediatric brain tumors?
PURPOSE: Pediatric brain tumors, including high-grade gliomas (HHG), medulloblastomas (MB), and ependymomas (EPN), are a leading cause of death in children. They are often immunologically "cold" with low tumor mutational burden (TMB) and very few tumor-infiltrating lymphocytes (TILs), which may limit the role of immune checkpoint inhibitors (ICI). METHODS: We performed a PRISMA‑guided systematic review of PubMed/MEDLINE, Embase, and Scopus from inception to September 17, 2025, for English-language studies of patients ≤ 21 years with primary CNS tumors treated with PD‑1/PD‑L1 or CTLA‑4 inhibitors. Eligible reports included prospective trials, retrospective series, and observational/case reports with extractable data on efficacy and/or toxicity. Of 479 records identified, 386 unique citations were screened, 127 underwent full‑text review, and 40 met inclusion criteria for qualitative synthesis. RESULTS: Prospective and institutional studies in biomarker-unselected diffuse midline glioma, high-grade glioma, medulloblastoma, and ependymoma showed low objective response rates (generally ≤ 6%), short median progression-free survival (1-3 months), and overall survival similar to historical controls, despite acceptable safety (grade ≥ 3 treatment-related adverse events ~ 15-25% with anti-PD-1 ± anti-CTLA-4). In contrast, across germline MMR-deficient and broader RRD/hypermutant cohorts, PD-1 blockade produced clinically meaningful and sometimes durable responses, including complete remissions in malignant glioma and approximate 2-year overall survival near 50%, often with delayed responses and pseudoprogression. Histology-specific profiling highlighted marked variation in immune contexture: pediatric gliomas segregate into immune "hot," "altered," and "cold" subtypes; medulloblastoma is largely PD-L1-low with prominent B7-H3 and myeloid programs; checkpoint expression is also observed in germ cell and selected sellar tumors. Evidence quality is limited by small, heterogeneous, predominantly non-comparative designs. CONCLUSION: ICIs show manageable safety but limited efficacy in unselected pediatric CNS tumors. Durable benefit is most evident in RRD/hypermutant biology and possibly PD-L1-high niches (e.g., some low-grade gliomas and CNS-GCT). Future trials should be biomarker-driven and pair ICIs with priming combinations (e.g., radiation, epigenetic modulators, metronomic chemotherapy) to convert "cold" tumors into responders.
18 May 2026
Read appraisal →Endocrine-related cancer
Systematic review of the molecular basis for cavernous sinus invasion in somatotropinomas
Somatotropinomas are a subtype of pituitary adenomas that have a particular predilection to invade the cavernous sinus. The objective of this systematic review was to examine the evidence regarding the molecular basis for cavernous sinus invasion in somatotropinomas. This review was conducted in accordance with the 2020 PRISMA guidelines on 13 April 2025. Inclusion criteria were reports of associations between somatotropinoma molecular changes and cavernous sinus invasion in adult patients. Title/abstract screening and full-text screening were performed, with studies assessed for risk of bias using the Newcastle-Ottawa scale. A total of 43 studies were identified, encompassing 1,824 patients (724 invasive tumours). Overall, 33 studies identified molecules that were upregulated in invasive tumours and 20 studies identified molecules that were downregulated. Few studies incorporated modern proteomic or transcriptomic techniques. Risk of bias was low with a mean Newcastle-Ottawa scale score of 7.0 (±0.6). Molecules associated with invasion were related to epithelial-mesenchymal transition (E-cadherin, ESRP1, fascin-1, and MMP-9), cellular proliferation (PTTG, AIP, TCERG1, EIF2β, E2F1, Notch 2, STAT3, ARRB1, TGFB1, SMAD3, SOX9, SGK1, MAGEA6 DLL3, EGFL7, and pEGFR), hormonal signalling (GNAS, DRD5, DRD1, sst5TMD4, SSTR2, and SSTR5), and tumour angiogenesis (VEGF and Drp1). These molecular variations present a possible explanation for the proclivity of somatotropinomas for the cavernous sinus. Identified molecules represent options for novel targeted therapies or biomarkers that could inform prognostication. Modern proteomic and transcriptomic techniques and larger somatotropinoma datasets are required to further elucidate the molecular pathways responsible for cavernous sinus invasion in somatotropinomas.
13 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 →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 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 →Biomedical physics & engineering express
Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP
Accurate and efficient segmentation of brain tumors is critical for diagnosis, treatment planning, and monitoring in clinical practice. In this study, we present an enhanced ResU-Net architecture for automatic brain tumor segmentation, integrating an EfficientNetB0 encoder, a channel attention mechanism, and an atrous spatial pyramid pooling (ASPP) module. The EfficientNetB0 encoder leverages pre-trained features to improve feature extraction efficiency, while the channel attention mechanism enhances the model's focus on tumor-relevant features. ASPP enables multi-scale contextual learning, which is crucial for handling tumors of varying sizes and shapes. The proposed model was evaluated on two benchmark datasets: The Cancer Genome Atlas Low Grade Glioma and brain tumor segmentation (BraTS-2020). Experimental results demonstrate that our method consistently outperforms the baseline ResU-Net and its EfficientNet variant, achieving dice similarity coefficient of 0.903 and 0.851, and HD95 scores of 9.43 and 3.54 for whole tumor and tumor core (TC) regions on the BraTS 2020 dataset, respectively. Compared to state-of-the-art methods, our approach shows competitive performance, particularly in whole tumor and TC segmentation. These results indicate that combining a powerful encoder with attention mechanisms and ASPP can significantly enhance BraTS performance. The proposed approach holds promise for further optimization and application in other medical image segmentation tasks.
11 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 radiology
Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models
OBJECTIVES: To evaluate the performance of large language models (LLMs) in predicting molecular types of adult-type diffuse gliomas according to the 2021 WHO classification using MRI radiology reports. MATERIALS AND METHODS: This retrospective study included 2169 patients diagnosed with adult-type diffuse gliomas (294 oligodendrogliomas, 295 IDH-mutant astrocytomas, and 1580 IDH-wildtype glioblastomas) between July 2005 and March 2024 from four hospitals in Asia and Europe. Seven proprietary and open-source LLMs were assessed: GPT-4o-mini, GPT-4.1-mini, Llama 3.1 8B, Llama 3.1 70B, Qwen2.5 7B, Deepseek-r1 8B, and Mistal 7B. The performance of LLMs in classifying molecular types was compared based on the provision of relevant knowledge of glioma imaging findings (knowledge-based vs. naïve prompt). The impact of radiologists' subspecialization in neuro-oncology, report quality, and reporting language on LLMs' performance was also evaluated. RESULTS: LLMs achieved significantly higher (naïve vs. knowledge-based; GPT-4o-mini, 77.0% vs. 79.1%, p < 0.001; Qwen2.5 7B, 75.9% vs. 79.5%, p < 0.001; Deepseek-r1 8B, 66.0% vs. 73.2%, p < 0.001) or comparable accuracy (GPT-4.1-mini, 78.7% vs. 78.6%; Llama 3.1 70B, 78.0% vs. 78.1%; Mistral 7B, 58.4% vs. 57.4%) using knowledge-based prompt compared to naïve prompt, except for Llama 3.1 8B (65.4% vs. 44.6%, p < 0.001). Differences in accuracy were more pronounced in smaller-sized LLMs. Additionally, the accuracy was significantly higher with reports by neuro-oncology specialists and high-quality reports in all LLMs (p < 0.001). CONCLUSIONS: LLMs may provide preoperative information on the tumor types of adult-type diffuse gliomas from MRI reports by providing relevant knowledge in the prompt. Informative and descriptive reports could further enhance LLMs' performance. KEY POINTS: Question Our study aimed to evaluate large language models' (LLMs) ability to efficiently predict molecular types of adult-type diffuse gliomas according to the 2021 WHO classification. Findings Larger models generally showed better accuracy and were less sensitive to domain-specific knowledge. Their performance improved when using high-quality, longer reports or reports by neuro-oncology specialists. Clinical relevance These findings highlight the potential role of LLMs in predicting glioma molecular types, underscoring the importance of informative and descriptive reports in enhancing their performance.
4 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Conducting a randomized controlled clinical trial on palliative care in patients with glioblastoma - what are the challenges?
PURPOSE: Patients with glioblastoma represent a highly vulnerable cohort as they often experience rapid health deterioration with severe symptom burden including neurological, (neuro)psychological, and psychiatric symptoms. The aim of this sub-analysis of the "Early Palliative Care for Patients with Glioblastoma" (EPCOG) trial was to investigate the specific challenges of conducting a multicenter, randomized, controlled, clinical trial in glioblastoma patients testing a specialized palliative care (PC) intervention. METHODS: We analyzed screening protocols and protocol deviations with respect to number and reasons for non-participation, skipped/delayed visits and attrition using descriptive statistics and content analysis of free-text comments. RESULTS: In total, 41.5% of 556 screened patients were enrolled. Main reasons for non-participation were lack of interest (25.7%) and low functional status (11.5%). Attrition due to death (57.6%) was higher than due to illness (5.2%) or other reasons (21.2%). Main reasons for visit deviations were structural issues (in > 50% of neurosurgical visits), health status, and patient request. Protocol deviations showed that specialized PC intervention visits were least frequently skipped (4.5%) compared to study-specific outcome assessment (10.1%) and neurosurgical (43.3%) visits. Further, only 11.0% of the specialized PC intervention visits were delayed compared to 22.3% of the outcome assessment and 56.4% of the neurosurgical visits. CONCLUSION: In this clinical trial involving glioblastoma patients, a high level of motivation among the study participants could be reached, as reflected by low protocol deviations during the specialized PC intervention and study-specific outcome assessment visits. Reasons for this might be a close guidance as well as a patient and caregiver-oriented communication, e.g., by a personal contact of the PC team in the intervention group, personal outcome assessment visits at patients' whereabouts, or the inclusion of a study nurse at each site. Considering the high vulnerability of glioblastoma patients is crucial when designing and conducting clinical trials.
2 May 2026
Read appraisal →PloS one
LHW-Net: An ensemble-based machine learning framework for brain tumor classification
The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets.
24 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 →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 →Medicine
Glioma biomarker analysis and drug targeting prediction based on ferroptosis-related genes
Glioma represents a significant challenge in neuro-oncology due to its highly invasive nature and poor prognosis. Recent studies indicate that ferroptosis, a form of regulated cell death, plays a crucial role in the pathogenesis of glioma. Modulating ferroptosis may offer a novel therapeutic approach for glioma treatment. This study aims to identify ferroptosis-related biomarkers and patterns of immune infiltration in glioma using bioinformatics techniques, and to predict potential therapeutic drugs targeting these biomarkers. Gene expression profiles (GSE16011, GSE50161) were retrieved from the GEO database, while ferroptosis-related genes were sourced from the FerrDb database. Intersection genes were identified using the CytoHubba algorithm and a consensus clustering method. Immune infiltration was assessed using the CIBERSORT algorithm. Functional enrichment analysis and gene set enrichment analysis were conducted with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Drug targeting predictions were made based on identified hub genes. We identified 10 hub genes: TP53, RRM2, EZH2, CDKN1A, MYCN, KIF20A, BLM, GLS2, HMOX1, and GOT1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that ferroptosis is primarily associated with apoptosis, reactive oxygen metabolism, the p53 signaling pathway, and immune regulation. Immune infiltration analysis showed significant differences in the expression of plasma cells, CD8 T cells, follicular helper T cells, activated NK cells, and macrophages between groups. qRT-PCR validation in glioma cell lines confirmed the differential expression of key ferroptosis-related genes, supporting the robustness of our bioinformatic findings. Furthermore, molecular docking demonstrates excellent binding properties between the drug components and key targets, and these key targets were further validated by PCR analysis in vitro. In summary, this study highlights the potential of ferroptosis-related biomarkers in glioma and suggests novel drug targets, providing a foundation for future therapeutic strategies.
12 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 →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 →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A systematic review of the implementation of cancer-specific holistic needs assessment (HNA) in adult clinical practice, and applicability to the brain tumour population
PURPOSE: This review aimed to identify and review any brain tumour-specific holistic needs assessment (HNA) and the clinical implementation of any HNAs in an adult cancer population. METHODS: A systematic review and narrative synthesis. Five electronic databases were searched from 2008 to 2023 and updated in January 2025. Reference lists of systematic reviews were screened for relevant studies. Two independent reviewers performed study selection, critical appraisal, and data extraction. RESULTS: Four HNA tools implemented in clinical practice, across 9 studies, were included. One study was adapted for brain tumour patients. Evidence of widespread implementation in clinical practice was limited and varied evidence, relating to the timing, location and mode of delivering. Patients were more likely to report on physical rather than psychosocial needs, with limited evidence of patient and staff satisfaction with the use of HNA. CONCLUSION: There is limited and varied evidence of HNAs being successfully implemented in brain tumour and the wider cancer healthcare settings. Tailored interventions for the needs of people with brain tumour have the potential to address the complexities this population faces. However, clear evidence-based guidance to develop relevant HNAs for successful implementation in clinical settings is needed.
4 Apr 2026
Read appraisal →Neurological research
Machine learning-based preoperative prediction of IDH mutation status in adult gliomas using clinical features
BACKGROUND: As isocitrate dehydrogenase (IDH) mutation status represents a critical prognostic factor in adult gliomas, there is a demand for a straightforward but effective predictive model to facilitate rapid preoperative diagnosis. METHODS: A cohort of 418 adult glioma patients from our institution were retrospectively enrolled and randomly partitioned into training (70%) and internal validation (30%) sets. Among 9 machine learning algorithms evaluated, AdaBoost demonstrated superior predictive performance based on receiver operating characteristic (ROC) analysis and was subsequently selected as the optimal model. Through feature importance ranking derived from the model, the top 8 most contributory features were identified and incorporated into the final streamlined prediction model. The model's generalizability was further assessed using an independent external validation cohort comprising 206 adult glioma cases. RESULTS: The AdaBoost model demonstrated robust discriminative performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.901 in the internal validation cohort and 0.849 in the independent external validation cohort. SHAP (SHapley Additive exPlanations) interpretation revealed age as the most influential predictive feature, contributing significantly to the model's decision-making process. CONCLUSIONS: This validated AdaBoost model offers a practical and interpretable solution for preoperative IDH status assessment, demonstrating both diagnostic reliability and biological plausibility through its feature importance profile.
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 →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 →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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