AI and Cancer Research ── Brain ── 2026-10-30
Brain tumours: treatment, trials and AI
- Abstract
Introduction: Glioblastoma (GBM) commonly recurs close to the original gross tumor volume (GTV), suggesting treatment-resistant subregions may be present before radiologically visible progression. This study investigated whether deep learning on apparent diffusion coefficient (ADC) maps could generate pixel-wise probability maps of treatment-resistant regions within the GTV.
Methods: The training/validation set included images of 64 in-house patients after radiation fraction 10 (Fx10) and 40 external patients before treatment used as an auxiliary cross-domain training cohort; the held-out test set included 16 in-house Fx10 patients. Resistant regions were defined as the overlap between the GTV from scans acquired at radiation fraction 10 and subsequent recurrence after finishing treatment. Models were trained on 2D GTV-cropped slices with patient-level separation to prevent data leakage. Single- and multimodal MRI inputs were compared, and ADC-based models (including baseline UNet, pretrained ResNet34, BiomedCLIP-assisted UNet, and a BLIP image-text UNet) were later evaluated. Performance was evaluated using conventional metrics, including ROC AUC, PR AUC, F1-score, recall, specificity, and precision.
Results: ADC was the most informative single modality, achieving 69% and 80% for Receiver Operating Characteristic (ROC)- and Precision-Recall (PR)-Area Under the Curve (AUC), respectively. In the ADC-based architecture comparison, no model was consistently superior across all metrics. The baseline UNet achieved the highest accuracy, F1-score, and recall, whereas pretrained ResNet34 and BLIP-assisted models showed slightly higher ROC AUC than the baseline UNet (both 70%). Error analysis showed that performance decreased substantially for small or sparse resistant regions, and false-negative errors were enriched near resistant-region and GTV boundaries.
Conclusions: ADC-based deep learning showed feasibility for pixel-wise spatial mapping of treatment-resistant glioblastoma regions within the GTV. A UNet trained from scratch provided the strongest sensitivity/F1 trade-off, whereas pretrained and VLM-assisted models provided only metric-specific benefits. Model comparisons showed that BiomedCLIP-assisted and BLIP image-text UNet models did not clearly improve fine spatial segmentation performance over the baseline UNet.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Rezvanjou S, Osapoetra LO, Moore-Palhares D, Lawrence LSP, Lau AZ, Sahgal A, et al. Pixel-Wise Prediction of Glioblastoma Recurrence Using Mid-Treatment MRI and Deep Learning. Cancers. 2026 Oct 27;18(21):3465. doi:10.3390/cancers18213465.Checked: Abstract only - Abstract
Background/Objectives: Glioma diagnosis increasingly depends on the integration of magnetic resonance imaging (MRI), molecular characteristics, and clinical information. Foundation models may support AI-assisted diagnosis through transferable representation learning and multimodal integration, but evidence supporting clinical use remains fragmented. This systematic scoping review maps current applications of foundation models in glioma neuroimaging and evaluates their relevance to tumor delineation, molecular characterization, multimodal diagnostic support, and clinical translation. Following PRISMA-ScR principles, six bibliographic sources were searched through 26 July 2026. Eligible records were categorized by model family, pretraining and adaptation strategy, imaging application, multimodal integration, validation design, and clinical relevance. Among 3688 identified records, 2071 remained after deduplication, 525 underwent detailed eligibility assessment, and 125 were included. Evidence was concentrated in promptable segmentation (n = 47), reusable brain MRI representations (n = 26), vision language and language model applications (n = 22), molecular classification and prognosis prediction (n = 16), and translational or governance studies (n = 14). Foundation models showed potential for tumor delineation, reusable representation learning, molecular phenotype prediction, and multimodal diagnostic support. However, independent external validation, robustness to imaging heterogeneity and missing sequences, calibration, and prospective workflow evaluation remain limited. Foundation models offer promising capabilities for AI-assisted glioma diagnosis, particularly in segmentation, reusable MRI representation learning, molecular characterization, and multimodal support. Current evidence is nevertheless insufficient for routine clinical implementation. Multicenter validation, standardized evaluation, uncertainty assessment, and prospective studies of diagnostic and workflow utility should be priorities for future research.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Yu Y, Zhang P, Xia C, Ma X, Xie F. Foundation Models for Glioma MRI: A Systematic Scoping Review of Clinical Applications and Translational Readiness. Brain Sci. 2026 Oct 26;16(11):1159. doi:10.3390/brainsci16111159.Checked: Abstract only