{"doi":"10.1093/neuonc/noaf189","title":"AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides","abstract":"BACKGROUND: The 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). METHODS: Our 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. RESULTS: Our 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. CONCLUSION: Determining 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.","journal":"Neuro-Oncology","year":2025,"id":542200,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6277,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":453212,"name":"William R. Bell","orcid":"0000-0001-6326-3885","position":1,"is_corresponding":false},{"id":1028824,"name":"MacLean P Nasrallah","orcid":null,"position":2,"is_corresponding":false},{"id":762297,"name":"Bhakti Baheti","orcid":"0000-0001-5475-3903","position":3,"is_corresponding":false},{"id":103874,"name":"Spyridon Bakas","orcid":"0000-0001-8734-6482","position":4,"is_corresponding":false},{"id":1038265,"name":"Shubham Innani","orcid":"0000-0003-3616-0308","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T02:52:55.809501Z","pmid":"40888157","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}