Predicting survival after hepatocellular carcinoma resection using deep‐learning on histological slides

Standardized and robust risk stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation. In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (WSI) to build models for predicting the survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n=194) used to develop our algorithms and an independent validation set (TCGA, n=328). WSIs were first divided into small squares ("tiles") and features were extracted with a pretrained convolutional neural network (preprocessing step). The first deep-learning based algorithm ("SCHMOWDER") uses an attention mechanism on tumoral areas annotated by a pathologist while the second ("CHOWDER") does not require human expertise. In the discovery set, c-indexes for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75, respectively. Both models outperformed a composite score incorporating all baseline variables associated with survival. The prognostic value of the models was further validated in the TCGA dataset, and, as observed in the discovery series, both models had a higher discriminatory power than a score combining all baseline variables associated with survival. Pathological review showed that the tumoral areas most predictive of poor survival were characterized by vascular spaces, the macrotrabecular architectural pattern and a lack of immune infiltration. CONCLUSION: This study shows that artificial intelligence can help refine the prediction of HCC prognosis. It highlights the importance of pathologist/machine interactions for the construction of deep-learning algorithms that benefit from expert knowledge and allow a biological understanding of their output.