Machine learning methods to understand hepatocellular carcinoma pathology

Hepatocellular carcinoma (HCC) is one of the commonest fatal tumors, and it is usually diagnosed at a late stage, when effective treatment is very difficult. Unfortunately early diagnosis of HCC is almost mandatory in terms of patient survival, but it represents a very difficult task. Detailed histological characteristics of small HCC and precursor lesions in histological diagnosis are needed to refine the contribution of histopathology to the management of patients with HCC. To enhance transparency and interpretability of the results, we applied non-blackbox machine learning algorithms on a set of 180 diagnosed cases of liver nodular lesions.