Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning

Frontiers in Oncology

Yongguang Liu¹  Wei Gao²  Kaimei Huang⁴  Yan Wu⁴  Yachao Yang⁵ 

[1] Department of Anorectal Surgery, Weifang People’s Hospital, Weifang, China;Department of Internal Medicine-Oncology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, China;Genies (Beijing) Co., Ltd., Beijing, China;Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China;School of Mathematics and Statistics, Hainan Normal University, Haikou, China;

关键词: immunotherapy;    colorectal cancer;    deep learning;    tumor mutational burden;    ResNet;   

DOI  :  10.3389/fonc.2022.906888

来源: DOAJ

【 摘 要 】

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 the Residual Network (ResNet) 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 ×40 magnification images and grouped them by patients followed by thresholding at the 10th and 20th quantiles, 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.

【 授权许可】

Unknown