Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Accurate prediction of patient overall survival (OS) from histopathology whole slide images (WSI) integrated with clinical data using advanced computational methods could optimize clinical decision-making and patient management. Here, we focus on identifying prognostically relevant glioblastoma characteristics from H&E stained WSI & clinical data relating to OS. The exact approach for WSI capitalizes on the comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple-instance learning algorithm that further utilizes clustering to constrain the search space. The automatically placed pat- terns of high diagnostic value classify each WSI as representative of short or long-survivors. Further assessment of the prognostic relevance of the associated clinical patient data is performed both in isolation and in an integrated manner, using XGBoost and SHapley Additive exPlanations (SHAP). Identifying tumor morphological & clinical patterns associated with short and long OS will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for understanding and potentially treating glioblastoma.

翻译：胶质母细胞瘤是中枢神经系统最常见且最具侵袭性的成人恶性肿瘤，其预后极差且具有异质性形态学和分子学特征。自18年前采用当前标准治疗方案以来，患者预后未出现实质性改善。利用先进计算方法整合组织病理学全切片图像与临床数据准确预测患者总生存期，可优化临床决策和患者管理。本研究聚焦于从H&E染色的全切片图像及与总生存期相关的临床数据中识别具有预后意义的胶质母细胞瘤特征。全切片图像处理方法的核心在于：通过人工伪影内容的全面筛选，并借助基于弱监督注意力机制的多实例学习算法实现可解释性机制——该算法进一步利用聚类约束搜索空间。自动识别的高诊断价值模式可将每张全切片图像归类为短期或长期生存者代表。我们采用XGBoost和SHapley Additive exPlanations方法，对相关临床患者数据的预后相关性分别进行独立评估和整合分析。识别与短期/长期总生存期相关的肿瘤形态学及临床模式，将有助于临床神经病理学家向治疗团队提供额外的预后信息，并为理解及潜在治疗胶质母细胞瘤提出生物学研究方向。