Kumar S. Artificial intelligence powered radiomics model for the assessment of colorectal tumor immune microenvironment. World J Gastrointest Oncol 2025; 17(11): 108576 [DOI: 10.4251/wjgo.v17.i11.108576]
Corresponding Author of This Article
Shashank Kumar, PhD, Professor, Department of Biochemistry, Central University of Punjab, VPO Ghudda Central University of Punjab Lab no 520, Bathinda 151401, Punjab, India. shashankbiochemau@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
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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/
Nov 15, 2025 (publication date) through Nov 13, 2025
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Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Oncology
ISSN
1948-5204
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Kumar S. Artificial intelligence powered radiomics model for the assessment of colorectal tumor immune microenvironment. World J Gastrointest Oncol 2025; 17(11): 108576 [DOI: 10.4251/wjgo.v17.i11.108576]
World J Gastrointest Oncol. Nov 15, 2025; 17(11): 108576 Published online Nov 15, 2025. doi: 10.4251/wjgo.v17.i11.108576
Artificial intelligence powered radiomics model for the assessment of colorectal tumor immune microenvironment
Shashank Kumar
Shashank Kumar, Department of Biochemistry, Central University of Punjab, Bathinda 151401, Punjab, India
Author contributions: Kumar S wrote the original draft and conceptualization; reviewing and editing.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Shashank Kumar, PhD, Professor, Department of Biochemistry, Central University of Punjab, VPO Ghudda Central University of Punjab Lab no 520, Bathinda 151401, Punjab, India. shashankbiochemau@gmail.com
Received: April 18, 2025 Revised: May 8, 2025 Accepted: June 20, 2025 Published online: November 15, 2025 Processing time: 210 Days and 20.1 Hours
Core Tip
Core Tip: The present hospital-based retrospective research designed an artificial intelligence- and pathological data-based predictive model to make preoperative immunotherapy decisions in colorectal cancer patients. The study includes a small number of individuals from a single medical center. Study claims deep learning radiomics models based on tumor immune microenvironment assessment for personalized immunotherapy decisions in colorectal cancer patients. The study lacks inclusion and exclusion criteria, particularly the exclusion of patients having other malignancies and prior treatment/immunotherapy status. The analysis should look at the clinicopathological features (age, sex, how well the tumor is differentiated, stage, lymph node status, lymphovascular invasion, and perineural invasion) of patients in both the training and validation groups. These metrics will yield valuable insights for therapeutic practice.
