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Histopathology-based survival modelling has two major hurdles. Firstly, awell-performing survival model has minimal clinical application if it does notcontribute to the stratification of a cancer patient cohort into different riskgroups, preferably driven by histologic morphologies. In the clinical setting,individuals are not given specific prognostic predictions, but are ratherpredicted 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 wholeslide images were never utilized to their fullest extent due to technologicalconstraints on data processing, forcing decoupled learning. EPIC-Survivalbridges encoding and aggregation into an end-to-end survival modellingapproach, while introducing stratification boosting to encourage the model tonot only optimize ranking, but also to discriminate between risk groups. Inthis study we show that EPIC-Survival performs better than other approaches inmodelling intrahepatic cholangiocarcinoma, a historically difficult cancer tomodel. Further, we show that stratification boosting improves further improvesmodel performance, resulting in a concordance-index of 0.880 on a held-out testset. Finally, we were able to identify specific histologic differences, notcommonly sought out in ICC, between low and high risk groups.

EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting