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World J Gastroenterol. Sep 7, 2026; 32(33): 118584
Published online Sep 7, 2026. doi: 10.3748/wjg.118584
Artificial intelligence-integrated multimodal data-assisted magnetic resonance imaging for neoadjuvant chemoradiotherapy decision-making in cT1-2N0 rectal cancer
Bo-Yu Kang, He Bai, Ke Ni, Yi-Huan Qiao, Yun-Long Li, Yi-Qian Wang, Qi Wang, Jun Zhu, Ji-Peng Li
Bo-Yu Kang, Yi-Huan Qiao, Yun-Long Li, Qi Wang, Jun Zhu, Ji-Peng Li, Department of Digestive Surgery, The First Affiliated Hospital of Digestive Diseases, Air Force Medical University, Xi’an 710032, Shaanxi Province, China
Bo-Yu Kang, Yi-Huan Qiao, State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, National Clinical Research Center for Digestive Diseases, The First Affiliated Hospital of Air Force Medical University, Xi’an 710032, Shaanxi Province, China
He Bai, Department of General Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, Shaanxi Province, China
Ke Ni, School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710032, Shaanxi Province, China
Yi-Qian Wang, School of Medicine, South China University of Technology, Guangzhou 510000, Guangdong Province, China
Yi-Qian Wang, Department of General Surgery, The Sixth Medical Center of PLA General Hospital, Beijing 100000, China
Jun Zhu, Department of General Surgery, The Southern Theater Air Force Hospital, Guangzhou 510000, Guangdong Province, China
Ji-Peng Li, Department of Experiment Surgery, The First Affiliated Hospital of Air Force Medical University, Xi’an 710032, Shaanxi Province, China
Co-first authors: Bo-Yu Kang and He Bai.
Co-corresponding authors: Jun Zhu and Ji-Peng Li.
Author contributions: Kang BY and Bai H played important roles in the experimental design as co-first authors; Kang BY, Bai H, and Ni K performed experiments and analysis, wrote and revised the manuscript; Qiao YH, Li YL, Wang YQ, and Wang Q contributed to follow-up and data analysis; Zhu J and Li JP equally contributed to the research and study design as co-corresponding authors; all authors read and approved the final manuscript.
Supported by National Natural Science Foundation of China, No. 82172781; Shaanxi Provincial Health Scientific Research Innovation Team Project, No. CBSKL2022ZZ44; and Scientific and Technological Innovation Team of Shaanxi Innovation Capability Support Plan, No. 2023-CX-TD-67.
Institutional review board statement: The study protocol adhered to the ethical guidelines of the 1995 Declaration of Helsinki, and this study was approved by the Ethics Committee of the First Affiliated Hospital of Air Force Medical University (No. KY20232232-C-1).
Informed consent statement: Given the retrospective nature of the cohort and the anonymization of all data, individual informed consent was waived.
Conflict-of-interest statement: The authors declare no conflict of interest in publishing the manuscript.
Data sharing statement: The data will be available after publication by contacting the corresponding author. Sharing is restricted to researchers at academic institutions, and the use of the analyses is limited to non-commercial scientific analyses only, and the use of the data for patent applications is prohibited.
Corresponding author: Ji-Peng Li, MD, PhD, Chief Physician, Postdoc, Professor, Department of Digestive Surgery, The First Affiliated Hospital of Digestive Diseases, Air Force Medical University, No. 127 Changle West Road, Xincheng District, Xi’an 710032, Shaanxi Province, China.
jipengli1974@aliyun.com
Received: January 6, 2026
Revised: January 30, 2026
Accepted: April 8, 2026
Published online: September 7, 2026
Processing time: 215 Days and 12.4 Hours
BACKGROUND
Colorectal cancer represents a major global health burden, with neoadjuvant chemoradiotherapy as standard treatment for locally advanced rectal cancer, although preoperative magnetic resonance imaging (MRI) staging shows only moderate accuracy. A substantial proportion of patients staged as cT1-2N0 on MRI are subsequently upstaged after surgery, thereby missing neoadjuvant treatment and risking worse oncologic outcomes.
AIM
To develop a machine learning model that integrates multiomics profiles to improve the accuracy of neoadjuvant chemoradiotherapy decision-making in rectal cancer patients whose baseline MRI indicates cT1-2N0 disease.
METHODS
This multicenter cohort study consecutively enrolled patients who underwent pre-operative MRI and curative rectal cancer surgery at three institutions between January 2013 and December 2024. Participants were randomly allocated to training, internal validation and external validation sets. A pre-defined set of 2260 radiomic features was extracted from T2-weighted and diffusion-weighted images and fused with baseline clinical, hematological and pathological variables. Ten independent machine learning classifiers were constructed and compared with both physician decisions and imaging-only radiomic models. Model performance was evaluated with receiver operating characteristic analysis, calibration plots, decision curve analysis and confusion matrices.
RESULTS
A total of 1320 consecutive patients with clinically staged cT1-2N0 rectal cancer who underwent curative intent surgery were retrospectively enrolled from three tertiary centers: (1) Center 1 (n = 1009); (2) Center 2 (n = 246); and (3) Center 3 (n = 65). We developed a Clinical, Hematologic, Oncopathologic, and Radiomic Decision (CHORD) model, an XGBoost-based machine learning classifier that integrates seven preoperative MRI radiomic features with 16 clinical, hematologic, and pathologic variables via a CART regression tree algorithm in patients with cT1-2N0 rectal cancer. Externally validated, the CHORD model delivered an area under the curve of 0.927 and an F1-score of 0.825, attesting to its consistent and robust performance across independent cohorts.
CONCLUSION
Our study demonstrated that the CHORD model exhibits satisfactory performance in identifying cT1-2N0 rectal cancer patients who require neoadjuvant chemoradiotherapy by integrating preoperative multi-omics data, offering critical insights into reducing the omission rate of neoadjuvant therapy and enhancing the accuracy of preoperative radiological staging.
Core Tip: We developed an artificial intelligence-driven multimodal model that not only improves radiomic performance but also provides clinicians with a reliable tool to reduce missed identification of patients who would benefit from neoadjuvant therapy. Compared with radiologist assessment, the model demonstrates improved reliability and clear advantages in supporting clinical decision-making.
