Predicting Tumor Mutational Burden from Liver Cancer Pathological Images Using Convolutional Neural Network

Tumor mutational burden (TMB) is the most important and most promising biomarker in the era of tumor immunotherapy, and it can predict the immunotherapy efficiency of patients in various cancers including liver cancer. TMB is mainly obtained by next generation sequencing technology such as whole exome sequencing (WES). However, conditions such as excessive testing costs, lengthy detection cycles, and tissue sample dependence severely limit the clinical application of TMB. Inspired by the inner link between the intrinsic characteristics of the tumor cell genome and the pathological features of tumor cells and their microenvironment-related cells, we propose a deep learning method for predicting the level of TMB (high or low) directly from pathological images. This study found that the feature scale (receptive field) is the biggest factor affecting the classification effect of TMB prediction, and further determined the best receptive field through a series of experiments. Experimental results show that our method is far more out performance of the commonly used panel sequencing (99.7% VS 79.2%). To the best of our knowledge, this is the first research to predict TMB and the highest level of accuracy of genomic characteristic predicted by pathological images. The proposed method has the potential to provide immunotherapy to a much broader subset of patients with liver cancer.