Artificial intelligence for brain cancer management: multimodal digital workflows for diagnosis, treatment planning, and longitudinal monitoring.
Authors
Affiliations (15)
Affiliations (15)
- Scojen Institute for Synthetic Biology, Dina Recanati School of Medicine, Reichman University, Herzliya, Israel.
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Department of Pathology, Faculty of Medicine, Pavol Jozef Safarik University in Kosice, Kosice, Slovakia.
- Department of Computer Engineering, EPOKA University, Tirana, Albania; Deoartment of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, Rome 00133, Italy.
- Department of Computer Engineering, EPOKA University, Tirana, Albania.
- School of Food Science and Environmental Health, Physical to Life Sciences Research Hub, Nano Lab Research Centre, Technological University Dublin, Dublin, Ireland.
- Mathematics Department and Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal.
- Department of Applied Mathematics and Biomedical Imaging Laboratory, Kaunas University of Technology, Kaunas, Lithuania.
- Department of Biology, Faculty of Science, Dokuz Eylül University, İzmir, Turkey.
- Department of Mathematics & Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Edificio Politecnico, Avda. Camilo Jose Cela s/n, Ciudad Real, Castilla La-Mancha, Spain.
- School of Health and Life Sciences, Teesside University, Middlesbrough TX1 3BX, UK.
- Department of Biology and Ecology, Faculty of Sciences and Mathematics, University of Niš, Niš, Serbia.
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK.
- LR21ES03 "Oncogenesis and Tumour Progression", Faculty of Medicine of Sousse, University of Sousse, Sousse, Tunisia.
- Scojen Institute for Synthetic Biology, Dina Recanati School of Medicine, Reichman University, Herzliya, Israel. Electronic address: [email protected].
Abstract
Brain cancers, especially glioblastoma, remain among the deadliest adult cancers, with outcomes largely unchanged despite multimodal treatments. This review summarizes cutting-edge artificial intelligence (AI) and machine learning (ML) advances transforming neuro-oncology in diagnostics, molecular profiling, treatment planning, and monitoring. Key findings show AI-driven radiomics and deep learning (DL) reaching over 90% accuracy in tumour segmentation and grading from MRI and whole-slide images, non-invasive IDH/MGMT prediction through liquid biopsy (LB) analysis, and augmented reality-guided resection that maximizes tumour removal while safeguarding expressive cortex. Treatment planning benefits from hybrid Convolutional Neural Networks (CNN)-Transformer models for immunotherapy stratification and blood-brain barrier penetrant drug repurposing, while real-time progression detection via multimodal integration helps differentiate true progression from pseudoprogression. Despite these advances, significant challenges remain, including data scarcity and imbalance in rare subtypes, domain shift due to imaging variability, black-box model behaviour eroding trust, regulatory requirements for prospective validation, and workflow fragmentation. Emerging solutions include federated and transfer learning for scalable model development, explainable AI (such as SHapley Additive exPlanations (SHAP) and attention interpretation for vision transformers) to foster clinician-AI collaboration, and foundation models pretrained on large-scale neuro-oncology datasets to facilitate personalization. Achieving this potential will depend on harmonized multi-omics registries, strong ethical and regulatory governance, and deliberate human-AI collaboration frameworks to integrate these tools into precision neuro-oncology.