Histopathology-based survival modelling has two major hurdles. Firstly, a well-performing survival model has minimal clinical application if it does not contribute to the stratification of a cancer patient cohort into different risk groups, preferably driven by histologic morphologies. In the clinical setting, individuals are not given specific prognostic predictions, but are rather predicted to lie within a risk group which has a general survival trend. Thus, It is imperative that a survival model produces well-stratified risk groups. Secondly, until now, survival modelling was done in a two-stage approach (encoding and aggregation). The massive amount of pixels in digitized whole slide images were never utilized to their fullest extent due to technological constraints on data processing, forcing decoupled learning. EPIC-Survival bridges encoding and aggregation into an end-to-end survival modelling approach, while introducing stratification boosting to encourage the model to not only optimize ranking, but also to discriminate between risk groups. In this study we show that EPIC-Survival performs better than other approaches in modelling intrahepatic cholangiocarcinoma, a historically difficult cancer to model. Further, we show that stratification boosting improves further improves model performance, resulting in a concordance-index of 0.880 on a held-out test set. Finally, we were able to identify specific histologic differences, not commonly sought out in ICC, between low and high risk groups.

本研究使用 EPIC-Survival 对肝内胆管癌进行预后建模，实现了对不同风险组的分层和排名，改进了传统预后模型刻画能力，同时揭示了低风险组和高风险组的组织学差异。

EPIC-Survival: 基于部位推断的端到端生存分析聚类，具有预后分层提升