Artificial intelligence for predicting TMB-H colorectal cancer from histopathological characteristics Biomarkers are important determinants of appropriate and effective therapeutic approaches for various diseases including cancer. There is ample evidence pointing toward the significance of immune check point inhibitors (ICI) against cancer, and they showed promising clinical benefits to a specific group of patients with colorectal cancer (CRC). Several reports demonstrated the efficacy of biomarkers such as programmed death-1 protein ligand (PD-L1), density of tumor-infiltrating lymphocytes (TILs), and tumor mutational burden (TMB), to determine the patient responsiveness for the efficient use of ICIs as therapeutics against cancer. A high level of TMB (TMB-H), which reflects elevated total number of non-synonymous somatic mutations per coding area of a tumor genome and normally derived from gene panel testing, is recognized as a promising biomarker for the ICI therapies of various solid cancers. However, in clinical practice, it is not feasible to perform gene panel testing for all cancer patients.