AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides.

backgroundThe WHO 2021 classification criteria for adult-type diffuse glioma integrate histology with molecular profiling for conclusive diagnosis. Since molecular profiling can be expensive and time-consuming, often necessitating outsourcing or leading to the "not otherwise specified (NOS) label," this study develops an AI-driven WHO 2021 classification of gliomas solely from H&E whole-slide images (WSIs).

methodsOur pipeline is based on a multi-institutional dataset reclassified per WHO 2021 guidelines. This dataset includes (a) Primarily US-based TCGA-GBM/TCGA-LGG (n = 1,320) for model training, independently evaluated on two hold-out sets, (b) Austria-based EBRAINS (n = 794), and (c) India-based IPD-Brain (n = 304). Each WSI undergoes pre-processing followed by quantitative benchmarking across (i) eight pathology foundation models (FM), (ii) nine aggregation methods (AM), and (iii) 15 combinations of magnification levels through a late fusion approach. Model interpretability conducted through heatmaps highlights distinct, identifiable morphology features.

resultsOur best-performing combination of FM, AM, and multi-magnification achieved an AUC of 97.95% on the training cohort, 96.30% on EBRAINS (set 1), and 92.61% on IPD (set 2). The results yield the following key insights: (1) domain-specific FMs outperform ImageNet-based models, (2) AMs, while theoretically promising yield larger performance improvements when used with ImageNet-based feature extractor rather than FMs, and (3) Fusion of multiple magnifications adds value in performance.

conclusionDetermining glioma diagnosis directly from H&E slides can obviate the need for molecular profiling, expedite conclusive diagnosis, and, hence, clinical decision-making. These findings motivate the development of advanced domain-relevant FMs and the design of more adaptable slide-level aggregation techniques.