Editorial
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jul 15, 2025; 17(7): 108175
Published online Jul 15, 2025. doi: 10.4251/wjgo.v17.i7.108175
Deep learning radiomics: Redefining precision oncology through noninvasive insights into the tumor immune microenvironment
Mesut Tez
Mesut Tez, Department of Surgery, University of Health Sciences, Ankara City Hospital, Ankara 06800, Türkiye
Author contributions: Tez M wrote and finalized the manuscript.
Conflict-of-interest statement: All the authors report 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: Mesut Tez, Department of Surgery, University of Health Sciences, Ankara City Hospital, No. 1 Bilkent Street, District of Universities, Ankara 06800, Türkiye. mesuttez@yahoo.com
Received: April 7, 2025
Revised: April 17, 2025
Accepted: May 13, 2025
Published online: July 15, 2025
Processing time: 98 Days and 9.4 Hours
Core Tip

Core Tip: Computed tomography-based deep learning radiomics provides a noninvasive, scalable approach to predict the tumor immune microenvironment in colorectal cancer, achieving high accuracy (area under the curve: 0.851-0.892) in the study by Zhou et al. By overcoming biopsy limitations, the approach revolutionizes personalized immunotherapy, chemotherapy, and targeted therapies, integrating advanced imaging with immune profiling to redefine precision oncology and improve patient outcomes.