RANO working group outlines AI validation priorities across neuro-oncology diagnostics
Investigators from the Response Assessment in Neuro-Oncology (RANO) working groups published a comprehensive review mapping artificial intelligence applications across six neuro-oncology domains, including neuropathology, laboratory medicine, and liquid biopsy interpretation. The review identifies imaging segmentation, DNA methylation-based tumor classification, and workflow optimization as the most clinically mature tools, while noting that radiogenomics, whole-slide image molecular prediction, and liquid biopsy analysis remain promising but require robust prospective validation. The authors emphasize that standardizing data acquisition, ensuring multi-institutional external validation, and maintaining human oversight are critical next steps for integrating AI into routine diagnostic workflows and clinical trials.
The original study
AI in Neuro-Oncology: An Overarching Review.
- Authors
- Pitarch-Abaigar C, Lohmann P, Aloy P, Al-Shahrour F, Arnaout O, Bi WL, et al.
- Journal
- Neuro-oncology
- Type
- Journal Article
- PMID
- 42867147
Original abstract
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.