Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. Histomorphology is a crucial component of the integrated diagnosis of GBM-IDHwt. Artificial intelligence (AI) methods have shown promise to extract additional prognostic information from histological whole-slide images (WSI) of hematoxylin and eosin-stained glioblastoma tissue. Here, we present an explainable AI-based method to support systematic interpretation of histomorphological features associated with survival. It combines an explainable multiple instance learning (MIL) architecture with a sparse autoencoder (SAE) to relate human-interpretable visual patterns of tissue to survival. The MIL architecture directly identifies prognosis-relevant image tiles and the SAE maps these tiles post-hoc to visual patterns. The MIL method was trained and evaluated using a new real-world dataset that comprised 720 GBM-IDHwt cases from three hospitals and four cancer registries in Germany. The SAE was trained using 1878 WSIs of glioblastoma from five independent public data collections. Despite the many factors influencing survival time, our method showed some ability to discriminate between patients living less than 180 days or more than 360 days solely based on histomorphology (AUC: 0.67; 95% CI: 0.63-0.72). Cox proportional hazards regression confirmed a significant difference in survival time between the predicted groups after adjustment for established prognostic factors (hazard ratio: 1.47; 95% CI: 1.26-1.72). Our method identified multiple interpretable visual patterns associated with survival. Three neuropathologists separately found that 21 of the 24 most strongly associated patterns could be clearly attributed to seven histomorphological categories. Necrosis and hemorrhage appeared to be associated with shorter survival while highly cellular tumor areas were associated with longer survival.

翻译：IDH野生型胶质母细胞瘤（GBM-IDHwt）是最常见的恶性脑肿瘤。组织形态学是GBM-IDHwt整合诊断的关键组成部分。人工智能方法已展现出从苏木精-伊红染色的胶质母细胞瘤组织全切片图像中提取额外预后信息的潜力。本文提出一种基于可解释人工智能的方法，用于系统解读与生存期相关的组织形态学特征。该方法将可解释多示例学习架构与稀疏自编码器相结合，将人类可解读的组织视觉模式与生存期建立关联。MIL架构直接识别与预后相关的图像区块，SAE则将这些区块后验映射到视觉模式。MIL方法使用德国三家医院和四个癌症登记处的720例GBM-IDHwt病例构成的新现实世界数据集进行训练和评估。SAE使用来自五个独立公共数据集的1878张胶质母细胞瘤WSI进行训练。尽管存在诸多影响生存时间的因素，我们的方法仅基于组织形态学在区分生存期短于180天或长于360天的患者方面表现出一定能力（AUC：0.67；95% CI：0.63-0.72）。Cox比例风险回归在调整既定预后因素后，证实预测组间的生存时间存在显著差异（风险比：1.47；95% CI：1.26-1.72）。我们的方法识别出多个与生存期相关的可解释视觉模式。三位神经病理学家独立发现，24个最强相关模式中有21个可明确归属于七个组织形态学类别。坏死和出血似乎与较短生存期相关，而高细胞密度肿瘤区域则与较长生存期相关。