INNV-35. Artificial intelligence in Neuro-Oncology: Mapping the field

Abstract BACKGROUND Artificial intelligence (AI) is reshaping neuro-oncology research and clinical practice. This abstract summarizes key findings from a peer-reviewed review article (accepted, The Lancet Digital Health), which maps AI applications across the neuro-oncological care trajectory and critically examines major opportunities, challenges, and future directions. METHODS We searched PubMed, ArXiv, and Google Scholar using comprehensive MeSH term-based search strings (e.g., “glioma,” “machine learning,”, “foundation model,” “omics”) from 1/1/2020–12/7/2024. Article metadata were retrieved via Python wrappers (built around PubMed and ArXiv APIs) or manually (from Google Scholar). Records were screened and deduplicated. Studies were selected based on predefined criteria, including explicit use of machine learning (ML) as a core technology, a multicentric or independent validation cohort, and high methodological rigor. RESULTS Screening of 2,675 unique records revealed that current AI-neuro-oncology literature primarily focuses on clinical neuroimaging or omics, often using radiomics, deep learning, or traditional ML, with fewer studies investigating advanced generative models. Analysis of 52 original articles meeting inclusion criteria identified robust AI applications in medical image analysis (e.g., non-invasive diagnosis and response assessment), digital neuropathology, biomarker discovery, tumor phenotyping, patient risk stratification, personalized precision treatment, and neuro-rehabilitative devices. Exploratory approaches include generalist and agentic neuro-oncology assistants, biophysical and causal models (e.g., for neural–cancer dynamics), synthetic data, and drug (target) discovery. Barriers to full integration include major data gaps, limited clinical validation of current tools, and unresolved ethical, legal, and regulatory issues. CONCLUSIONS Promising AI use cases are emerging across the neuro-oncological care trajectory, although current data, validation, and implementation gaps limit clinical deployment and scaling beyond narrowly defined tasks, particularly for advanced generalist models. Closing these gaps will require addressing data collection, standardization and annotation challenges; prioritizing rigorous prospective validation to demonstrate improved clinical outcomes; and grounding tool development in human-centred, ethical, and agile regulatory frameworks for responsible innovation.