The application of machine learning and deep learning radiomics in the treatment of esophageal cancer

Abstract

Esophageal cancer (EC) is a very aggressive disease with most cases diagnosed at advanced stages. Early detection and prognosis prediction are of clinical significance in the optimal management of EC. Genomic and proteomic technologies demonstrated limited efficacy due to the invasive nature and the inherent tumor heterogeneity. Non-invasive radiomics has achieved significant results in tumor characterization, treatment response and survival prediction for various cancers. In this article, the current application of both machine learning and deep learning based radiomics in the diagnosis, prognostic prediction and treatment outcome prediction for patients with EC were reviewed. The current challenges and prospects for the future application of radiomics in EC were also discussed.