We previously established mouse models of biliary tract cancer (BTC) based on the injection of cells with biliary epithelial stem cell properties derived from KRAS(G12V)-expressing organoids into syngeneic mice. The resulting mouse tumors appeared to recapitulate the pathological features of human BTC. Here we analyzed images of hematoxylin and eosin (H&E) staining for both the mouse tumor tissue and human cholangiocarcinoma tissue by pixel-level clustering with machine learning. A pixel-clustering model that was established via training with mouse images revealed homologies of tissue structure between the mouse and human tumors, suggesting similarities in tumor characteristics independent of animal species. Analysis of the human cholangiocarcinoma tissue samples with the model also revealed that the entropy distribution of cancer regions was higher than that of noncancer regions, with the entropy of pixels thus allowing discrimination between these two types of regions. Histograms of entropy tended to be broader for noncancer regions of late-stage human cholangiocarcinoma. These analyses indicate that our mouse BTC models are appropriate for investigation of BTC carcinogenesis and may support the development of new therapeutic strategies. In addition, our pixel-level clustering model is highly versatile and may contribute to the development of a new BTC diagnostic tool.
Keywords: biliary tract cancer; cholangiocarcinoma; entropy; homology; mouse model; pixel clustering.