<h4>Objective</h4>To explore the utility of a 2.5D deep transfer learning (DTL) model for distinguishing between Kirsten rat sarcoma viral oncogene (KRAS) mutant and wild-type phenotypes in patients with rectal cancer (RC).<h4>Methods</h4>We retrospectively analyzed 138 patients with pathologically confirmed RC who underwent next-generation sequencing to detect KRAS mutations. The gene discussed is KRAS; the disease is rectal cancer.