This study aimed to identify key image parameters from the traditional and advanced MR sequences within the peritumoral edema in glioblastoma, which could predict the sub-volume with high risk of tumor recurrence. The retrospective cohort involved 32 cases with recurrent glioblastoma, while the retrospective validation cohort consisted of 5 cases. The volume of interest (VOI) including tumor and edema were manually contoured on each MR sequence. Rigid registration was performed between sequences before and after tumor recurrence. The edema before tumor recurrence was divided into the subedema-rec and subedema-no-rec depending on whether tumors occurred after registration. The histogram parameters of VOI on each sequence were collected and statistically analyzed. Beside Spearman's rank correlation analysis, Wilcoxon's paired test, least absolute shrinkage and selection operator (LASSO) analysis, and a forward stepwise logistic regression model(FSLRM) comparing with two machine learning models was developed to distinguish the subedema-rec and subedema-no-rec. The efficiency and applicability of the model was evaluated using receiver operating characteristic (ROC) curve analysis, image prediction and pathological detection. Differences of the characteristics from the ADC map between the subedema-rec and subedema-no-rec were identified, which included the standard deviation of the mean ADC value (stdmeanADC), the maximum ADC value (maxiADC), the minimum ADC value (miniADC), the Ratio-maxiADC/meanADC (maxiADC divided by the meanADC), and the kurtosis coefficient of the ADC value (all P < 0.05). FSLRM showed that the area under the ROC curve (AUC) of a single-parameter model based on Ratio-maxiADC/meanADC (0.823) was higher than that of the support vector machine (0.813) and random forest models (0.592), compared to the retrospective validation cohort's AUC of 0.776. The location prediction in image revealed that tumor recurrent mostly in the area with Ratio-maxiADC/meanADC less than 2.408. Pathological detection in 10 patients confirmed that the tumor cell dotted within the subedema-rec while not in the subedema-no-rec. The Ratio-maxiADC/meanADC is useful in predicting location of the subedema-rec.

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being applied in medical research, including studies on cerebral cavernous malformations (CCM). This scoping review aims to analyze the scope and impact of AI in CCM, focusing on diagnostic tools, risk assessment, biomarker identification, outcome prediction, and treatment planning. We conducted a comprehensive literature search across different databases, reviewing articles that explore AI applications in CCM. Articles were selected based on predefined eligibility criteria and categorized according to their primary focus: drug discovery, diagnostic imaging, genetic analysis, biomarker identification, outcome prediction, and treatment planning. Sixteen studies met the inclusion criteria, showcasing diverse AI applications in CCM. Nearly half (47%) were cohort or prospective studies, primarily focused on biomarker discovery and risk prediction. Technical notes and diagnostic studies accounted for 27%, concentrating on computer-aided diagnosis (CAD) systems and drug screening. Other studies included a conceptual review on AI for surgical planning and a systematic review confirming ML's superiority in predicting clinical outcomes within neurosurgery. AI applications in CCM show significant promise, particularly in enhancing diagnostic accuracy, risk assessment, and surgical planning. These advancements suggest that AI could transform CCM management, offering pathways to improved patient outcomes and personalized care strategies.

Accurate ischemic stroke lesion segmentation is useful to define the optimal reperfusion treatment and unveil the stroke etiology. Despite the importance of diffusion-weighted MRI (DWI) for stroke diagnosis, learning from multi-sequence MRI images like apparent diffusion coefficient (ADC) can capitalize on the complementary nature of information from various modalities and show strong potential to improve the performance of segmentation. However, existing deep learning-based methods require large amounts of well-annotated data from multiple modalities for training, while acquiring such datasets is often impractical. We conduct the exploration of semi-supervised stroke lesion segmentation from multi-sequence MRI images by utilizing unlabeled data to improve performance using limited annotation and propose a novel framework by exploiting cross-modality collaboration and discrepancy to efficiently utilize unlabeled data. Specifically, we adopt a cross-modal bidirectional copy-paste strategy to enable information collaboration between different modalities and a cross-modal discrepancy-informed correction strategy to efficiently learn from limited labeled multi-sequence MRI data and abundant unlabeled data. Extensive experiments on the ischemic stroke lesion segmentation (ISLES 22) dataset demonstrate that our method efficiently utilizes unlabeled data with 12.32% DSC improvements compared with a supervised baseline using 10% annotations and outperforms existing semi-supervised segmentation methods with better performance.

BackgroundAccurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patient expectations. The prognostic utility of neuroimaging and neurophysiological biomarkers remains uncertain when measured early post-stroke. This systematic review aimed to examine the prognostic capacity of early neuroimaging and neurophysiological biomarkers of mobility outcomes up to 24-months post-stroke. MethodsMEDLINE and EMBASE were searched from inception to June 2025. Cohort studies that reported neuroimaging or neurophysiological biomarkers measured [&le;]14-days post-stroke and mobility outcome(s) assessed >14-days and [&le;]24-months post-stroke were included. Biomarker analyses were classified by statistical analysis approach (association, discrimination/classification or validation). Magnitude of relevant statistical measures was used as the primary indicator of prognostic capacity. Risk of bias was assessed using the Quality in Prognostic Studies tool. Meta-analysis was not performed due to heterogeneity. ResultsTwenty reports from 18 independent study samples (n=2,160 participants) were included. Biomarkers were measured a median 7.5-days post-stroke, and outcomes were assessed between 1- and 12-months. Eighty-six biomarker analyses were identified (61 neuroimaging, 25 neurophysiological) and the majority used an association approach (88%). Few used discrimination/classification methods (11%), and only one conducted internal validation (1%); an MRI-based machine learning model which demonstrated excellent discrimination but still requires external validation. Structural and functional corticospinal tract integrity were frequently investigated, and most associations were small or non-significant. Lesion location and size were also commonly examined, but findings were inconsistent and often lacked magnitude reporting. Methodological limitations were common, including small sample sizes, moderate to high risk of bias, poor reporting of magnitudes, and heterogeneous outcome measures and follow-up time points. ConclusionsCurrent evidence provides limited support for early neuroimaging and neurophysiological biomarkers to prognosticate post-stroke mobility outcomes. Most analyses remain at the association stage, with minimal progress toward validation and clinical implementation. Advancing the field requires international collaboration using harmonized methodologies, standardised statistical reporting, and consistent outcome measures and timepoints. RegistrationURL: https://www.crd.york.ac.uk/prospero/; Unique identifier: CRD42022350771.

To develop an accessible model integrating clinical, MRI, and radiomic features to predict periventricular leukomalacia (PVL) in high-risk infants. Two hundred and seventeen infants (2015-2022) with suspected motor abnormalities, stratified into training (n = 124), internal validation (n = 31), and external validation (n = 62) cohorts by MRI scanners. Radiomic features were extracted from white matter regions on axial sequences. Feature selection employed T-tests, correlation filtering, Random Forest, and LASSO regression. Multivariate logistic models were evaluated by receiver operating characteristic (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, calibration, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). Clinical predictors (gestational age, neonatal hypoglycemia, hypoxic-ischemic events, infection) and MRI features (dilated lateral ventricle, delayed myelination, and periventricular abnormal signal) were retained through univariate and multivariate screening. Five clinical predictive models, including clinical model (Model C), MRI model (Model M), Clinical + MRI model (Model C + M), radiomic model and Clinical + MRI + Radiomics model (Model C + M + R), were developed and validated using internal testing, bootstrapping, and external cohorts. Among them, Model C + M + R achieved the best overall performance, with an area under curve (AUC) of 0.96 (95% CI: 0.90-1.00), accuracy of 0.87 (95% CI: 0.76-0.94), sensitivity of 0.88, specificity of 0.85, PPV of 0.96, and NPV of 0.65 in the external validation cohort. Comparison with Model C + M, Model C + M + R demonstrated significant reclassification (NRI = 0.631, p < 0.001) and discrimination improvements (IDI = 0.037, p = 0.020). Conventional MRI-derived radiomics enhances PVL risk stratification. Interpretable accessible model for clinical use provides a new tool for high-risk infant evaluation. Question Periventricular leukomalacia requires early identification to optimize neurorehabilitation. Early white matter injury in infants is challenging to identify through conventional MRI visual assessment. Findings The clinical-MRI-radiomic model demonstrates the best performance for predicting PVL, with an AUC of 0.93 in the training and 0.96 in the external validation cohort. Clinical relevance An accessible and interpretable predictive tool for periventricular leukomalacia prediction has been developed and validated, which may enable earlier targeted interventions.

A significant challenge in solid tumors is reliably distinguishing confounding pathologies from malignant neoplasms on routine imaging. While radiomics methods seek surrogate markers of lesion heterogeneity on CT/MRI, many aggregate features across the region of interest (ROI) and miss complex spatial relationships among varying intensity compositions. We present a new Graph-Radiomic Learning (GrRAiL) descriptor for characterizing intralesional heterogeneity (ILH) on clinical MRI scans. GrRAiL (1) identifies clusters of sub-regions using per-voxel radiomic measurements, then (2) computes graph-theoretic metrics to quantify spatial associations among clusters. The resulting weighted graphs encode higher-order spatial relationships within the ROI, aiming to reliably capture ILH and disambiguate confounding pathologies from malignancy. To assess efficacy and clinical feasibility, GrRAiL was evaluated in n=947 subjects spanning three use cases: differentiating tumor recurrence from radiation effects in glioblastoma (GBM; n=106) and brain metastasis (n=233), and stratifying pancreatic intraductal papillary mucinous neoplasms (IPMNs) into no+low vs high risk (n=608). In a multi-institutional setting, GrRAiL consistently outperformed state-of-the-art baselines - Graph Neural Networks (GNNs), textural radiomics, and intensity-graph analysis. In GBM, cross-validation (CV) and test accuracies for recurrence vs pseudo-progression were 89% and 78% with >10% test-accuracy gains over comparators. In brain metastasis, CV and test accuracies for recurrence vs radiation necrosis were 84% and 74% (>13% improvement). For IPMN risk stratification, CV and test accuracies were 84% and 75%, showing >10% improvement.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types is critical for effective treatment planning. Magnetic Resonance Imaging (MRI) remains a primary diagnostic modality due to its non-invasive nature and ability to provide detailed brain imaging. However, traditional tumor classification relies on expert interpretation, which is time-consuming and prone to subjectivity. This study proposes a novel deep learning architecture, the Dual-Feature Cross-Fusion Network (DF-CFN), for the automated classification of brain tumors using MRI data. The model integrates ConvNeXt for capturing global contextual features and a shallow CNN combined with Feature Channel Attention Network (FcaNet) for extracting local features. These are fused through a cross-feature fusion mechanism for improved classification. The model is trained and validated using a Kaggle dataset encompassing four tumor classes (glioma, meningioma, pituitary, and non-tumor), achieving an accuracy of 99.33%. Its generalizability is further confirmed using the Figshare dataset, yielding 99.22% accuracy. Comparative analyses with baseline and recent models validate the superiority of DF-CFN in terms of precision and robustness. This approach demonstrates strong potential for assisting clinicians in reliable brain tumor classification, thereby improving diagnostic efficiency and reducing the burden on healthcare professionals.

To compare the diagnostic performance of radiomic features extracted from T2-weighted and arterial-phase T1-weighted MRI sequences using univariate, machine and deep learning analysis and to assess their effectiveness in predicting axillary lymph node (ALN) metastasis in breast cancer patients. We retrospectively analyzed MRI data from 100 breast cancer patients, comprising 52 metastatic and 103 non-metastatic lymph nodes. Radiomic features were extracted from T2-weighted and subtracted arterial-phase T1-weighted images. Feature normalization and selection were performed. Various machine learning classifiers, including logistic regression, gradient boosting, random forest, and neural networks, were trained and evaluated. Diagnostic performance was assessed using metrics such as area under the curve (AUC), sensitivity, specificity, and accuracy. T2-weighted imaging provided strong performance in multivariate modeling, with the neural network achieving the highest AUC (0.978) and accuracy (91.1%), showing statistically significant differences over models. The stepwise logistic regression model also showed competitive results (AUC = 0.796; accuracy = 73.3%). In contrast, arterial-phase T1-weighted imaging features performed better when analyzed individually, with the best univariate AUC reaching 0.787. When multivariate modeling was applied to arterial-phase features, the best-performing logistic regression model achieved an AUC of 0.853 and accuracy of 77.8%. Radiomic analysis of T2-weighted MRI, particularly through deep learning models like neural networks, demonstrated the highest overall diagnostic performance for predicting metastatic ALNs. In contrast, arterial-phase T1-weighted features showed better results in univariate analysis. These findings support the integration of radiomic features, especially from T2-weighted sequences, into multivariate models to enhance noninvasive preoperative assessment.

Glioblastoma (GBM) carries poor prognosis; epidermal-growth-factor-receptor (EGFR) mutations further shorten survival. We propose a fully automated MRI-based decision-support system (DSS) that segments GBM and classifies EGFR status, reducing reliance on invasive biopsy. The segmentation module (UNet SI) fuses multiresolution, entropy-ranked shearlet features with CNN features, preserving fine detail through identity long-skip connections, to yield a Lightweight 1.9 M-parameter network. Tumour masks are fed to an Inception ResNet-v2 classifier via a 512-D bottleneck. The pipeline was five-fold cross-validated on 98 contrast-enhanced T1-weighted scans (Memorial Hospital; Ethics 24.12.2021/008) and externally validated on BraTS 2019. On the Memorial cohort UNet SI achieved Dice 0.873, Jaccard 0.853, SSIM 0.992, HD95 24.19 mm. EGFR classification reached Accuracy 0.960, Precision 1.000, Recall 0.871, AUC 0.94, surpassing published state-of-the-art results. Inference time is ≤ 0.18 s per slice on a 4 GB GPU. By combining shearlet-enhanced segmentation with streamlined classification, the DSS delivers superior EGFR prediction and is suitable for integration into routine clinical workflows.

Multiple sclerosis (MS) is common in adults while myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare. Our previous machine-learning algorithm, using clinical variables, ≤6 brain lesions, and no Dawson fingers, achieved 79% accuracy, 78% sensitivity, and 80% specificity in distinguishing MOGAD from MS but lacked validation. The aim of this study was to (1) evaluate the clinical/MRI algorithm for distinguishing MS from MOGAD, (2) develop a deep learning (DL) model, (3) assess the benefit of combining both, and (4) identify key differentiators using probability attention maps (PAMs). This multicenter, retrospective, cross-sectional MAGNIMS study included scans from 19 centers. Inclusion criteria were as follows: adults with non-acute MS and MOGAD, with high-quality T2-fluid-attenuated inversion recovery and T1-weighted scans. Brain scans were scored by 2 readers to assess the performance of the clinical/MRI algorithm on the validation data set. A DL-based classifier using a ResNet-10 convolutional neural network was developed and tested on an independent validation data set. PAMs were generated by averaging correctly classified attention maps from both groups, identifying key differentiating regions. We included 406 MRI scans (218 with relapsing remitting MS [RRMS], mean age: 39 years ±11, 69% F; 188 with MOGAD, age: 41 years ±14, 61% F), split into 2 data sets: a training/testing set (n = 265: 150 with RRMS, age: 39 years ±10, 72% F; 115 with MOGAD, age: 42 years ±13, 61% F) and an independent validation set (n = 141: 68 with RRMS, age: 40 years ±14, 65% F; 73 with MOGAD, age: 40 years ±15, 63% F). The clinical/MRI algorithm predicted RRMS over MOGAD with 75% accuracy (95% CI 67-82), 96% sensitivity (95% CI 88-99), and specificity 56% (95% CI 44-68) in the validation cohort. The DL model achieved 77% accuracy (95% CI 64-89), 73% sensitivity (95% CI 57-89), and 83% specificity (95% CI 65-96) in the training/testing cohort, and 70% accuracy (95% CI 63-77), 67% sensitivity (95% CI 55-79), and 73% specificity (95% CI 61-83) in the validation cohort without retraining. When combined, the classifiers reached 86% accuracy (95% CI 81-92), 84% sensitivity (95% CI 75-92), and 89% specificity (95% CI 81-96). PAMs identified key region volumes: corpus callosum (1872 mm³), left precentral gyrus (341 mm³), right thalamus (193 mm³), and right cingulate cortex (186 mm³) for identifying RRMS and brainstem (629 mm³), hippocampus (234 mm³), and parahippocampal gyrus (147 mm³) for identifying MOGAD. Both classifiers effectively distinguished RRMS from MOGAD. The clinical/MRI model showed higher sensitivity while the DL model offered higher specificity, suggesting complementary roles. Their combination improved diagnostic accuracy, and PAMs revealed distinct damage patterns. Future prospective studies should validate these models in diverse, real-world settings. This study provides Class III evidence that both a clinical/MRI algorithm and an MRI-based DL model accurately distinguish RRMS from MOGAD.