Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning - Liu, Yongguang; Huang, Kaimei; Yang, Yachao; Wu, Yan; Gao, Wei (Frontiers in Oncology, 2022)
Colorectal cancer (CRC) is one of the most prevalent malignancies and immunotherapy can be applied to CRC patients of all ages, while its efficacy is uncertain. Tumor mutational burden (TMB) is important for predicting the effect of immunotherapy. Currently, whole-exome sequencing (WES) is a standard method to measure TMB, but it is costly and inefficient. Therefore, it is urgent to explore a method to assess TMB without WES to improve immunotherapy outcomes. In this study, we propose a deep learning method DeepHE based on Residual Network (Res-Net) model. On images of tissue, DeepHE can efficiently identify and analyze characteristics of tumor cells in CRC to predict the TMB. In our study, we used a ×40 large-scale image and grouped them by patients which significantly improves the performance. Also, our model is superior compared with multiple models. In summary, deep learning methods can explore the association between histopathological images and genetic mutations, which will contribute to the precise treatment of CRC patients.