AI in Neuro-Oncology: An Overarching Review
Artificial intelligence (AI) is increasingly being applied across neuro-oncology domains to analyze complex, high-dimensional, multimodal data. Despite major advances in the biological understanding and classification of CNS tumors, clinical outcomes for many of them remain poor, reflecting intra- and inter-tumor heterogeneity and limitations of conventional reductionist approaches. AI offers a paradigm shift to capture this heterogeneity and provide quantitative, reproducible diagnostic, predictive, and prognostic insights. This article describes a joint effort across the Response Assessment in Neuro-Oncology (RANO) working groups to provide an overarching review of the AI use across six key domains of neuro-oncology: neuroimaging, neuropathology, laboratory medicine, neurosurgery, drug discovery, and clinical trials. Applications closest to clinical use include imaging segmentation and quantification, AI-assisted image reconstruction, DNA methylation-based classification, and workflow optimization. Radiogenomics, WSI-based molecular prediction, AI-guided surgical targeting, liquid biopsy interpretation, AI-guided drug discovery, digital twins, and in silico trials remain promising but require stronger prospective validation. Key challenges include limited availability of large diverse datasets, variability in data acquisition and reference standards, and limited external validation. This review provides a unifying framework for understanding the role of AI across neuro-oncology and highlights key considerations for its responsible implementation, emphasizing standardization, reproducibility, multi-institutional validation, and human oversight.
Authors
- Patrick Aloy (ORCID: https://orcid.org/0000-0002-3557-0236)
- Wenya Linda Bi
- Jason T. Huse (ORCID: https://orcid.org/0000-0003-4514-0640)
- Fátima Al‐Shahrour (ORCID: https://orcid.org/0000-0003-2373-769X)
- Philipp Karschnia (ORCID: https://orcid.org/0000-0002-1254-5310)
- Spyridon Bakas (ORCID: https://orcid.org/0000-0001-8734-6482)
- Norbert Galldiks (ORCID: https://orcid.org/0000-0002-2485-1796)
- MacLean P. Nasrallah (ORCID: https://orcid.org/0000-0003-4861-0898)
- Manuel Valiente (ORCID: https://orcid.org/0000-0002-0647-7542)
- Felix Sahm (ORCID: https://orcid.org/0000-0001-5441-1962)
- Raymond Y. Huang (ORCID: https://orcid.org/0000-0001-7661-797X)
- Areeba Patel (ORCID: https://orcid.org/0000-0003-0374-4227)
- Joerg-Christian Tonn
- Philipp Lohmann
- Omar Arnaout
- Timothy R Smith
- Rifaquat Rahman
- Mark Zarella
- Renato Umeton
- Carla Pitarch-Abaigar
Institutions
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Indiana University Health (US)
- Brigham and Women's Hospital (US)
- St. Jude Children's Research Hospital (US)
- The University of Texas MD Anderson Cancer Center (US)
- Forschungszentrum Jülich (DE)
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Spanish National Cancer Research Centre (ES)
- Indiana Cancer Consortium (US)
- Institute for Research in Biomedicine (ES)
- Barcelona Institute of Science and Technology (ES)
- University Hospital Cologne (DE)
- Mass General Brigham (US)
- Indiana University – Purdue University Indianapolis (US)
- Technical University of Munich (DE)
- University of Pennsylvania (US)
- Ludwig-Maximilians-Universität München (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Neuro-Oncology
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1093/neuonc/noag257
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- Deutsche Forschungsgemeinschaft
- Generalitat de Catalunya
- National Institutes of Health
- Instituto de Salud Carlos III
- Agencia Estatal de Investigación
- National Cancer Institute
- NextGenerationEU