Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA. Combined hepatocellular-cholangiocarcinomas (cHCC-CCA) are challenging to diagnose, as they exhibit features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA). Here, the authors use deep learning to re-classify cHCC-CCA tumours into HCC or ICCA based on histopathology images.