Predicting Tumor Mutational Burden From Lung Adenocarcinoma Histopathological Images Using Deep Learning
Yi Niu, Lixia Wang, Xiaojie Zhang, Han Yu, Chunjie Yang, Henan Bai, Kaimei Huang, Changjing Ren, Geng Tian, Shengjie Yin, Yan Zhao, Ying Wang, Xiaoli Shi, Minghui Zhang
Abstract
Tumor mutation burden (TMB) is an important biomarker for tumor immunotherapy. It plays an important role in the clinical treatment process, but the gold standard measurement of TMB is based on whole exome sequencing (WES). WES cannot be done in most hospitals due to its high cost, long turnaround times and operational complexity. To seek out a better method to evaluate TMB, we divided the patients with lung adenocarcinoma (LUAD) in TCGA into two groups according to the TMB value, then analyzed the differences of clinical characteristics and gene expression between the two groups. We further explored the possibility of using histopathological images to predict TMB status, and developed a deep learning model to predict TMB based on histopathological images of LUAD. In the 5-fold cross-validation, the area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.64. This study showed that it is possible to use deep learning to predict genomic features from histopathological images, though the prediction accuracy was relatively low. The study opens up a new way to explore the relationship between genes and phenotypes.