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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 111971
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111971
Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.111971
Multiparametric magnetic resonance imaging-based predictive model for chemotherapy response in colorectal cancer patients with gene mutations
Wen-Yan Kang, Wen-Ming Deng, Yi-Hong Zhong, Xiao-Jun Li, Ling-Ling Feng, De-Hong Luo, Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China
Xiao-Qin Ye, Department of Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, Guangdong Province, China
Co-first authors: Wen-Yan Kang and Wen-Ming Deng.
Author contributions: Kang WY and Deng WM contributed equally to this article as co-first authors; Kang WY, Deng WM, and Luo DH contributed to conceptualization and design; Kang WY, Deng WM, Ye XQ, Zhong YH, Li XJ, Feng LL, and Luo DH contributed to material preparation, data acquisition, and analysis; all authors contributed to manuscript drafting and revision and approved the final version.
Supported by Shenzhen High-level Hospital Construction Fund.
Institutional review board statement: This study was approved by the Ethics Committee of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College in accordance with regulatory and ethical guidelines pertaining to retrospective research studies (Approval No. YW2022-21-3).
Informed consent statement: Informed consent was waived for this retrospective study due to the exclusive use of de-identified patient data, which posed no potential harm or impact on patient care.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data involved in the present study can be provided upon reasonable request.
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: De-Hong Luo, Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 113 Baohe Avenue, Longgang District, Shenzhen 518116, Guangdong Province, China.
Received: July 18, 2025
Revised: August 6, 2025
Accepted: September 4, 2025
Published online: October 15, 2025
Processing time: 92 Days and 0 Hours
Revised: August 6, 2025
Accepted: September 4, 2025
Published online: October 15, 2025
Processing time: 92 Days and 0 Hours
Core Tip
Core Tip: This study pioneers a multiparametric magnetic resonance imaging (MRI)-based predictive model tailored for colorectal cancer patients with gene mutations (e.g., KRAS, NRAS, and BRAF) to evaluate chemotherapy efficacy. By integrating tumor differentiation, T2 signal intensity ratio, tumor-to-anal margin distance, and MRI-detected lymph node metastasis, the model achieved high accuracy (area under the receiver operating characteristic curve > 0.93) in both training and validation sets, offering a non-invasive tool for personalized treatment planning.