The effective management of brain tumors relies on precise typing, subtyping, and grading. This study advances patient care with findings from rigorous multiple instance learning experimentations across various feature extractors and aggregators in brain tumor histopathology. It establishes new performance benchmarks in glioma subtype classification across multiple datasets, including a novel dataset focused on the Indian demographic (IPD- Brain), providing a valuable resource for existing research. Using a ResNet-50, pretrained on histopathology datasets for feature extraction, combined with the Double-Tier Feature Distillation (DTFD) feature aggregator, our approach achieves state-of-the-art AUCs of 88.08 on IPD-Brain and 95.81 on the TCGA-Brain dataset, respectively, for three-way glioma subtype classification. Moreover, it establishes new benchmarks in grading and detecting IHC molecular biomarkers (IDH1R132H, TP53, ATRX, Ki-67) through H&E stained whole slide images for the IPD-Brain dataset. The work also highlights a significant correlation between the model decision-making processes and the diagnostic reasoning of pathologists, underscoring its capability to mimic professional diagnostic procedures.

翻译：脑肿瘤的有效管理依赖于精确的分型、亚分型和分级。本研究通过在不同特征提取器和聚合器上进行严格的脑肿瘤组织病理学多实例学习实验，推动了患者护理的发展。它在多个数据集上建立了胶质瘤亚型分类的新性能基准，包括一个专注于印度人群的新数据集（IPD-Brain），为现有研究提供了宝贵资源。使用在组织病理学数据集上预训练的ResNet-50进行特征提取，结合双层特征蒸馏（DTFD）特征聚合器，我们的方法在IPD-Brain和TCGA-Brain数据集上分别实现了88.08和95.81的先进AUC值（用于三分类胶质瘤亚型分类）。此外，该方法在IPD-Brain数据集的H&E染色全切片图像上，为分级和检测IHC分子生物标志物（IDH1R132H、TP53、ATRX、Ki-67）建立了新基准。工作还凸显了模型决策过程与病理学家诊断推理之间的显著相关性，强调了其模拟专业诊断程序的能力。