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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 14, 2025; 31(42): 112180
Published online Nov 14, 2025. doi: 10.3748/wjg.v31.i42.112180
Published online Nov 14, 2025. doi: 10.3748/wjg.v31.i42.112180
Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram
Yu-Ming Liu, Yan-Yuan Du, Ying Song, Hong-Tai Xiong, Liu Cai, Su-Su Ma, Han-Yue Zhang, Rui-Ying Fang, Hong-Gang Zheng, Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
Hui-Bo Yu, Bai-Hui Li, Jin Gao, Beijing University of Chinese Medicine, Beijing 100029, China
Rui Cai, China-Japan Friendship Hospital, Beijing 100029, China
Co-first authors: Yu-Ming Liu and Yan-Yuan Du.
Author contributions: Liu YM, Du YY, Ma SS, Li BH and Cai R performed data curation; Liu YM, Du YY, Cai L, Zhang HY and Fang RY wrote the original draft; Liu YM, Du YY, Song Y, Xiong HT, Cai L, Ma SS, Yu HB, Gao J, Zhang HY and Fang RY contributed to review and editing; Zheng HG conceived and designed the study and performed the investigation.
Supported by the Beijing Municipal Natural Science Foundation, No. 7252262; High Level Chinese Medical Hospital Promotion Project, No. HLCMHPP2023085; National Natural Science Foundation of China, No. 82174463; National Administration of Traditional Chinese Medicine, No. ZYYCXTD-C-C202205; and China Academy of Chinese Medical Sciences, No. CI2021A01804 and No. 2022S469.
Institutional review board statement: This study has been approved by the Ethics Committee of Guang’anmen Hospital, China Academy of Chinese Medical Sciences (No. 2022-215-KY).
Informed consent statement: The informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on 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: Hong-Gang Zheng, MD, Doctor, Department of Oncology, Guang'an
Received: July 21, 2025
Revised: August 28, 2025
Accepted: October 14, 2025
Published online: November 14, 2025
Processing time: 116 Days and 15.8 Hours
Revised: August 28, 2025
Accepted: October 14, 2025
Published online: November 14, 2025
Processing time: 116 Days and 15.8 Hours
Core Tip
Core Tip: This study developed and validated the first clinic-machine learning (ML) nomogram for predicting chemotherapy-induced myelosuppression in colorectal cancer patients receiving first-line chemotherapy. By integrating clinical variables with multiple ML algorithms through a feature mapping algorithm, the model achieved high discrimination, good calibration, and consistent net clinical benefit. Unlike conventional nomograms or single-algorithm approaches, this clinic-ML nomogram combines interpretability with robust predictive accuracy, providing a practical decision-support tool to optimize individualized treatment strategies.