Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-exome sequencing is considered the gold standard for determining TMB, it is difficult to be applied in clinical practice due to its high cost. There are also a few DNA panel-based methods to estimate TMB; however, their detection cost is also high, and the associated wet-lab experiments usually take days, which emphasize the need for faster and cheaper alternatives.

Authors

  • Kaimei Huang
    Department of Mathematics, Zhejiang Normal University, Jinghua 321004, China.
  • Binghu Lin
    Department of General Surgery of Third Ward, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
  • Jinyang Liu
    Department of Sciences, Geneis (Beijing) Co., Ltd, Beijing 100102, China.
  • Yankun Liu
    Cancer Institute, Tangshan People's Hospital, Tangshan 063001, China.
  • Jingwu Li
    Cancer Institute, Tangshan People's Hospital, Tangshan 063001, China.
  • Geng Tian
    Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China.
  • Jialiang Yang
    Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China.