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.

翻译：以病理学为基础的生存建模有两大障碍。 首先,一个表现良好的生存建模在临床应用上很少,如果它不促进癌症患者组群分入不同的风险群体,最好是由历史形态驱动的。在临床环境中,没有给个人提供具体的预测预测,而是预测他们属于具有总体生存趋势的风险群体。因此,生存建模必须产生得到良好认可的风险群体。第二,到目前为止,一个表现良好的生存建模是在两个阶段(编码和汇总)中完成的。由于数据处理的技术限制,迫使分解学习,大量数字化的整个高幻灯片图像的像素从未被最充分地利用。EPIC-Survival的连接和整合为最终生存建模方法,同时引入分级提振,鼓励模型不仅优化排名,而且区分风险群体。在这项研究中,我们发现低PIC-Survival在模拟内部循环变异中比其他方法更好。 高化的整个幻灯片图像中的大量像素从未被最充分地利用,因为数据处理的技术限制,迫使人们分解学习。 EPIC-Survival联结,同时引入一个最终的模型,我们更难掌握了一种特定的癌症的模型。