AI and Cancer Research ── Brain ── 2026-10-17
Brain tumours: treatment, trials and AI
- Abstract
PURPOSE: The objective of this study was to develop and externally validate complementary magnetic resonance imaging (MRI)-based models and their stacked combinations for preoperative World Health Organization (WHO) grading of intracranial meningioma. METHODS: This retrospective multicenter study included 455 histopathologically confirmed meningiomas: a four-center development cohort (n = 346) with leave-one-center-out cross-validation and an external cohort (n = 109). Four base models used contrast-enhanced T1-weighted MRI: tumor + peritumoral radiomics, an nnU-Net segmentation-based classifier, frozen DINOv3 transfer-learning features, and clinical variables. All 15 combinations were prespecified and stacked using a linear meta-learner trained on development out-of-fold predictions. Frozen models were externally evaluated without modification. RESULTS: WHO 2-3 tumors comprised 24.6% overall (112/455; development 20.5%; external 37.6%). External AUCs were 0.732 for nnU-Net, 0.703 for DINOv3, 0.645 for clinical, 0.628 for radiomics. The multimodal ensemble (nnU-Net+DINOv3+Clinical) achieved the highest external AUC (0.742), exceeding radiomics (ΔAUC +0.114, p = 0.008) but not nnU-Net (ΔAUC +0.011, p = 0.737). Compared with the clinical model, discrimination was higher but nonsignificant at the prespecified threshold (ΔAUC +0.098, p = 0.050), while reclassification improved (IDI +0.109, p < 0.001). Radiomics-containing combinations performed strongly in development but lost most discrimination externally. The multimodal ensemble had an external calibration slope of 1.32 (Brier 0.190) and net benefit above default strategies at thresholds ≥ 0.23. CONCLUSION: The multimodal ensemble achieved the highest external AUC but did not significantly exceed nnU-Net, which was the most transportable single predictor of meningioma grade. Clinical variables did not improve discrimination but enabled interpretable feature-level attribution. Handcrafted radiomics lacked external transportability.
Journal IF-equivalent: 3.2 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Huryol C, Sulaimanov U, Yuce M, Sanlier N, Ozturk F, Ozturk O, et al. MRI-based preoperative WHO grade prediction in meningioma: development and comparison of deep learning, radiomics, clinical, and combined models in multicenter data. J Neurooncol. 2026 Oct 14;180(1):18. doi:10.1007/s11060-026-05827-6.Checked: Abstract only - Abstract
Purpose: To systematically review the integration of generative artificial intelligence (GenAI) in MRI-based brain tumor management and evaluate its impact on detection, segmentation, diagnosis, treatment planning, and outcome prediction. The review addresses the research question like How effectively can GenAI enhance MRI-based brain tumor management, and what challenges limit its clinical translation.
Methods: A systematic literature review was conducted following PRISMA guidelines across PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. Studies published between 2015 and 2025 that applied generative models—including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and transformer-based methods to brain tumor MRI analysis were included. Data were extracted from 55 peer-reviewed studies for qualitative synthesis.
Results: Hybrid architectures combining GANs with CNNs or transformers achieved classification accuracies up to 99.53%. Attention-based segmentation models on multi-modal MRI data attained Dice scores exceeding 0.89. GenAI demonstrated significant contributions to tumor detection, cross-modal synthesis, data augmentation, treatment personalization, and survival prediction. Key challenges included data scarcity, model explainability, algorithmic bias, and the need for prospective clinical validation.
Conclusions: GenAI offers transformative potential for neuro-oncological care by addressing data limitations and enabling personalized treatment strategies. However, resolving ethical concerns, ensuring regulatory compliance, and advancing multi-modal integration remain essential for successful clinical translation
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Advancing Brain Tumor Management Using Generative AI: A Comprehensive Review of MRI-Based Applications. Zenodo (CERN European Organization for Nuclear Research). [Epub ahead of print]. doi:10.5281/zenodo.23337543.Checked: Abstract only