Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with grim prognosis and heterogeneous morphologic and molecular profiles. Since the adoption of the current standard of care treatment, 18 years ago, there are no substantial prognostic improvements noticed. Accurate prediction of patient overall survival (OS) from clinical histopathology whole slide images (WSI) using advanced computational methods could contribute to optimization of clinical decision making and patient management. Here, we focus on identifying prognostically relevant glioblastoma morphologic patterns on H&E stained WSI. The exact approach capitalizes on the comprehensive WSI curation of apparent artifactual content and on an interpretability mechanism via a weakly supervised attention based multiple instance learning algorithm that further utilizes clustering to constrain the search space. The automatically identified patterns of high diagnostic value are used to classify the WSI as representative of a short or a long survivor. Identifying tumor morphologic patterns associated with short and long OS will allow the clinical neuropathologist to provide additional prognostic information gleaned during microscopic assessment to the treating team, as well as suggest avenues of biological investigation for understanding and potentially treating glioblastoma.
翻译:Glioblastoma是中神经系统最常见和最具侵略性的恶性成人肿瘤,其发病期为严酷的预感和混杂的血压和分子剖面图。自从18年前采用目前的护理治疗标准以来,没有观察到任何重大的预测性改进。从临床组织病理学临床组织病理学全片图像中准确预测病人总体生存(OS)有助于优化临床决策和病人管理。在这里,我们的重点是查明H & E 染色的 WSI上与血压瘤有关的血压瘤血压模式。精确的方法利用了WSI对表面文物内容和可解释性的全面整理,其方法是通过基于分散监督的多例学习算法,进一步利用聚合来限制搜索空间。自动确定的高诊断价值模式可用于将生命系统分类为短期或长期幸存者的代表。确定与短长的OS有关的肿瘤病理学模式,将使临床神经病理学家能够提供其他的预测性信息,在微生物学期间作为潜在研究途径的分类和治疗方法,建议对微生物学小组进行可能的治疗。