Krishnan A. Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer. World J Gastrointest Oncol 2025; 17(4): 102324 [DOI: 10.4251/wjgo.v17.i4.102324]
Corresponding Author of This Article
Arunkumar Krishnan, MD, Assistant Professor, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Apr 15, 2025; 17(4): 102324 Published online Apr 15, 2025. doi: 10.4251/wjgo.v17.i4.102324
Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer
Arunkumar Krishnan
Arunkumar Krishnan, Department of Supportive Oncology, Atrium Health Levine Cancer, Charlotte, NC 28204, United States
Author contributions: Krishnan A contributed to the concept of the study, as well as the review and editing of the manuscript; Krishnan A critically revised the manuscript for important intellectual content and approved the final version of the manuscript.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arunkumar Krishnan, MD, Assistant Professor, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Received: October 14, 2024 Revised: January 10, 2025 Accepted: January 16, 2025 Published online: April 15, 2025 Processing time: 162 Days and 0 Hours
Core Tip
Core Tip: In a recent study by Long et al, multiparametric magnetic resonance imaging and radiomics were utilized to anticipate the occurrence of metachronous liver metastasis in individuals newly diagnosed with rectal cancer. The random forest model, a predictive model component, demonstrated significant accuracy, achieving area under the curve values of 0.919 in the training cohort and 0.901 in the validation cohort, highlighting its potential for non-invasive risk assessment. By integrating radiomic features with clinical data, the model can support tailored treatment strategies and improve patient care. Nevertheless, it is important for future research to address methodological limitations, such as the exclusion of genomic markers, potential biases from the retrospective design, and the necessity for external validation across varied patient populations. Expanding the model to integrate multi-omic data and advanced imaging techniques has the potential to further its clinical significance and practicality.
