Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastroenterol. Apr 21, 2026; 32(15): 116121
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116121
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.116121
Letter to the Editor: Balancing efficacy and toxicity: The critical role of predictive models in colorectal cancer chemotherapy
Yi-Wei Qin, Da-Wei Chen, Department of Radiation Oncology, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong Province, China
Yi-Wei Qin, Peng-Wei Li, Xin-Yi Liang, You Mo, Da-Wei Chen, Cancer Hospital of Shandong First Medical University (Shandong Cancer Institute, Shandong Cancer Hospital), Jinan 250000, Shandong Province, China
You Mo, Department of Cardiology, The First Affiliated Hospital of Shantou University Medi
Co-first authors: Yi-Wei Qin and Peng-Wei Li.
Co-corresponding authors: You Mo and Da-Wei Chen.
Author contributions: Qin YW and Li PW contributed equally as co-first authors to this work. Specifically, Qin YW and Li PW jointly conceived the study’s perspective, drafted the initial manuscript, and were responsible for the critical revision and finalization of the intellectual content; Mo Y and Liang XY contributed to the conceptualization, writing, reviewing, and editing of the manuscript; Mo Y and Chen DW participated in drafting and revising the manuscript; Mo Y and Chen DW contributed equally as co-corresponding authors to this work. All authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Da-Wei Chen, MD, Professor, Researcher, Cancer Hospital of Shandong First Medical University (Shandong Cancer Institute, Shandong Cancer Hospital), No. 440 Jiyan Road, Jinan 250000, Shandong Province, China. dave0505@yeah.net
Received: November 3, 2025
Revised: December 10, 2025
Accepted: January 13, 2026
Published online: April 21, 2026
Processing time: 163 Days and 10.4 Hours
Revised: December 10, 2025
Accepted: January 13, 2026
Published online: April 21, 2026
Processing time: 163 Days and 10.4 Hours
Core Tip
Core Tip: We propose expanding Liu et al’s interpretable machine learning-based nomogram beyond myelosuppression to an integrated efficacy-toxicity platform that simultaneously forecasts tumor response and common multi-organ adverse events. By embedding these dual predictions into a real-time, utility-based clinical decision support system, designed for practical clinical use, clinicians can instantly identify the regimen offering each patient the maximal therapeutic index, thereby optimizing personalized treatment decisions.