Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 28, 2021; 27(32): 5306-5321
Published online Aug 28, 2021. doi: 10.3748/wjg.v27.i32.5306
Radiomics and machine learning applications in rectal cancer: Current update and future perspectives
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti, Simone Maurea
Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Simone Maurea, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
Pier Paolo Mainenti, Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
Author contributions: Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, and Maurea S equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Conflict-of-interest statement: The authors declare that they have no conflicting interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Valeria Romeo, MD, Academic Research, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Sergio Pansini 5, Naples 80131, Italy.
Received: January 27, 2021
Peer-review started: January 27, 2021
First decision: March 7, 2021
Revised: March 13, 2021
Accepted: July 22, 2021
Article in press: July 22, 2021
Published online: August 28, 2021
Core Tip

Core Tip: Rectal cancer is a common malignancy requiring a multidisciplinary approach to ensure the best clinical management. Diagnostic imaging has contributed to increased survival rates and provided crucial information on the course of rectal cancer patients. Artificial intelligence, and in particular radiomics and machine learning, are promising techniques that could further enhance the value of medical imaging, allowing the building of decision support tools based on quantitative data. We herein present and discuss the potential role of artificial intelligence in rectal cancer applied to different medical imaging modalities.