AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors

Artificial Intelligence (AI) is rapidly emerging as a transformative tool in medical research and practice. In neuro-oncology, AI may help to enhance diagnostic accuracy and reproducibility, manage complex multi-modal data, and facilitate personalized treatment.

This review aims to provide an overview of AI applications in the analysis of histopathological and molecular data of brain tumors.

Key applications in histopathology include molecular biomarker prediction from H&E stained slides, tumor classification, grading, and prognostication. In molecular pathology, the machine learning-driven DNA methylation-based classification of CNS tumors has already become an integral part of the most recent WHO classification. This framework is continuously refined by ongoing research identifying novel tumor types. Two examples of emerging applications are Stimulated Raman Histology (SRH) and nanopore sequencing. SRH enables an intraoperative AI-powered assessment of the histopathological phenotype. Nanopore sequencing can be used for fast molecular profiling of CNS tumors, including intraoperative methylation-based subtyping. Despite these significant advances, the clinical translation of AI tools faces some challenges, including the limited dataset availability, standardization and representativeness; the lack of robust external validation in many published studies; and the limited model interpretability. These challenges are currently being tackled by efforts to compile multi-institutional pathological datasets and by advances in explainable AI.

AI holds promise for advancing personalized neuro-oncology by improving diagnostic accuracy and accelerating existing workflows. Its potential to democratize access to precision diagnostics hinges on efforts to reduce the costs of digital infrastructure and facilitate specialized training.