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Review
. 2025 Aug 8:100876.
doi: 10.1016/j.landig.2025.100876. Online ahead of print.

Value of artificial intelligence in neuro-oncology

Sebastian Voigtlaender  1 , Thomas A Nelson  2 , Philipp Karschnia  3 , Eugene J Vaios  4 , Michelle M Kim  5 , Philipp Lohmann  6 , Norbert Galldiks  7 , Mariella G Filbin  8 , Shekoofeh Azizi  9 , Vivek Natarajan  10 , Michelle Monje  11 , Jorg Dietrich  12 , Sebastian F Winter  13
Affiliations
Review

Value of artificial intelligence in neuro-oncology

Sebastian Voigtlaender et al. Lancet Digit Health. .

Abstract

CNS cancers are complex, difficult-to-treat malignancies that remain insufficiently understood and mostly incurable, despite decades of research efforts. Artificial intelligence (AI) is poised to reshape neuro-oncological practice and research, driving advances in medical image analysis, neuro-molecular-genetic characterisation, biomarker discovery, therapeutic target identification, tailored management strategies, and neurorehabilitation. This Review examines key opportunities and challenges associated with AI applications along the neuro-oncological care trajectory. We highlight emerging trends in foundation models, biophysical modelling, synthetic data, and drug development and discuss regulatory, operational, and ethical hurdles across data, translation, and implementation gaps. Near-term clinical translation depends on scaling validated AI solutions for well defined clinical tasks. In contrast, more experimental AI solutions offer broader potential but require technical refinement and resolution of data and regulatory challenges. Addressing both general and neuro-oncology-specific issues is essential to unlock the full potential of AI and ensure its responsible, effective, and needs-based integration into neuro-oncological practice.

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Conflict of interest statement

Declaration of interests SV is an employee of QuantCo and may own stock as part of the standard compensation package. TAN receives consulting fees from Adya Health. EJV has received grants or contracts from Caris Life Sciences; consulting fees from Glasshouse Health and Servier Pharmaceuticals; is a member of the Data Safety Monitoring Board or Advisory Board of Servier Pharmaceuticals and NuvOx Pharma; is a member of ASTRO Finance and Audit Committee; and has received equipment, materials, drugs, medical writing, gifts, or other services from Caris Life Sciences. PL received speaker honoraria from Blue Earth Diagnostics; honoraria for Advisory Board participation from Servier Pharmaceuticals; and is a Chair of the EORTC Brain Tumor Group QA Committee. NG received speaker honoraria from Blue Earth Diagnostics; honoraria for Advisory Board participation at Telix Pharmaceuticals and Servier Pharmaceuticals; honoraria for consultancy services from Telix Pharmaceuticals; and is the Chair of the PET/RANO group and Chair of the EANO Publishing Activity Committee (both unpaid positions). SA and VN are employees of Alphabet and may own stock as part of the standard compensation package. MM holds equity in Maplight Therapeutics and CARGO Therapeutics. JD received royalties or licences from Wolters Kluwer (author for UpToDate); consulting fees from Novartis and Johnson & Johnson; and participated in a Data Safety Monitoring Board for Novartis and Janssen. All other authors declare no competing interests.

Figures

Figure 1.
Figure 1.
PRISMA flow diagram.
Figure 2.
Figure 2.
The distribution of machine learning methods (x-axis) per neuro-oncological data type (y-axis). The circle size is proportional to the number of abstracts referencing method-data type pairs in the 2675 pre-filtered articles. Our search identified that the current literature is largely dominated by studies on clinical imaging and radiomics/deep learning and classical statistical methods, which are also prominently used for omics studies; fewer studies investigate more niche modalities and advanced techniques. For raw data please see supplementary table 2, appendix p. 4. Abbreviations. CT: Computed tomography; dMRI: Diffusion MRI; ML: Machine learning; MRI: Magnetic resonance imaging; PET: Positron emission tomography Colour code. Blue: Clinical imaging (MRI, dMRI, CT, PET); purple: Neuropathology; green: Omics
Figure 3.
Figure 3.
Mapping key machine learning-based technologies along the neuro-oncological care trajectory.
See this image and copyright information in PMC

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