基于苏木精-伊红染色全切片图像的胶质瘤诊断多示例学习：一项印度队列研究
Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study
AI总结：
本研究针对脑肿瘤精准诊疗需求，基于印度人群IPD-Brain等数据集，结合ResNet-50与DTFD的多示例学习方法，提升了胶质瘤亚型分类等任务性能，且模型决策与病理医生推理具有相关性。
摘要：
脑肿瘤的有效管理依赖于精准的分型、亚型划分和分级。本研究通过在脑肿瘤组织病理学领域针对多种特征提取器和聚合器开展严谨的多示例学习实验，推动了患者诊疗水平的提升。研究在多个数据集上建立了胶质瘤亚型分类的新性能基准，其中包括一个聚焦印度人群的全新数据集IPD-Brain，为现有研究提供了宝贵资源。采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Double-Tier Feature Distillation（DTFD，双层特征蒸馏）特征聚合器，本方法在三分类胶质瘤亚型分类任务中，在IPD-Brain数据集上取得了88.08的AUC，在TCGA-Brain数据集上取得了95.81的AUC，均达到当前最优水平。此外，该研究还在IPD-Brain数据集上，通过H&E染色全切片图像，在胶质瘤分级以及IHC分子生物标志物（IDH1R132H、TP53、ATRX、Ki-67）检测方面建立了新基准。该研究还强调，模型的决策过程与病理科医生的诊断推理存在显著相关性，凸显了其模拟专业诊断流程的能力。
英文摘要：
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.
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