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World J Gastroenterol. Jul 7, 2026; 32(25): 119071
Published online Jul 7, 2026. doi: 10.3748/wjg.119071
Baseline multimodal clinical-radiomics-pathomics model predicts mucosal healing in Crohn’s disease after infliximab therapy
Xing-Wang Wu, Yong-Ping Cai, Yi Shen, Xiao-Min Zheng, Chang Rong, Kai-Cai Liu, Chao Zhu, Jing Hu, Jia-Xin Pei, Hui-Min Liu, Han Zhang
Han Zhang, Chang Rong, Xiao-Min Zheng, Yi Shen, Xing-Wang Wu, Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Hui-Min Liu, Jia-Xin Pei, Yong-Ping Cai, Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Jing Hu, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Chao Zhu, Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
Kai-Cai Liu, Department of Radiology Intervention, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
Co-first authors: Han Zhang and Hui-Min Liu.
Co-corresponding authors: Yong-Ping Cai and Xing-Wang Wu.
Author contributions: Zhang H and Liu HM contributed equally to this work as co-first authors. Their joint contributions included study design, data acquisition, statistical analysis, drafting of the original manuscript, and critical revision for important intellectual content; Cai YP and Wu XW contributed equally as co-corresponding authors. They jointly supervised the study, guided experimental design and data interpretation, and critically revised the manuscript; Zhang H and Liu HM conceived and supervised the study; Zhang H, Liu HM, Pei JX, Zhu C and Shen Y collecting data; Zhang H, Liu HM, Pei JX, Hu J, Zhu C, Liu KC, Zheng XM and Rong C analyzed data; Zhang H, Liu HM, Pei JX, Hu J, Zhu C, Liu KC and Zheng XM performed the statistical analysis; Hu J and Zhang H received the fund; Zhang H and Liu HM completed the design and conception starting from the article, the literature search in the early stages, as well as data analysis and article writing; Cai YP and Wu XW made manuscript revisions; all authors reviewed the results and approved the final version of the manuscript.
Supported by the Research Fund of Anhui Institute of Translational Medicine, No. 2023zhyx-C35; and Natural Science Foundation of Anhui Province, China, No. 2308085MH241.
Institutional review board statement: This retrospective study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (approval No. PJ2024-11-68).
Informed consent statement: This retrospective study was approved with a waiver of informed consent.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The unidentified computed tomography enterography, pathological measurement data and clinical data used to support the findings of this study were supplied by Wu XW under license and so cannot be made freely available. Requests for access to these data should be made to Wu XW (e-mail:
wuxingwang@ahmu.edu.cn).
Corresponding author: Xing-Wang Wu, MD, Doctor, Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Intersection of Tianhai Road and Feicui Road, Feixi County, Hefei 230022, Anhui Province, China.
wuxingwang@ahmu.edu.cn
Received: January 19, 2026
Revised: February 21, 2026
Accepted: March 17, 2026
Published online: July 7, 2026
Processing time: 163 Days and 21.5 Hours
BACKGROUND
Mucosal healing (MH) represents a crucial therapeutic objective in Crohn’s disease (CD). Infliximab (IFX) is extensively employed to induce MH; however, reliable instruments for the early prediction of MH remain scarce. Radiomics and pathomics have the capacity to extract high dimensional features from images and pathological specimens. We postulated that a multimodal model integrating radiomics, pathomics, and clinical data could precisely forecast MH in CD patients undergoing IFX therapy.
AIM
To develop and validate a multimodal model for predicting MH in patients with CD following IFX therapy.
METHODS
This retrospective investigation recruited 269 patients with confirmed CD (ileal: 102, with 71 for training and 31 for testing; ileocolonic: 167, with 116 for training and 51 for testing). Clinical variables, whole-slide image-derived pathomics features (used to calculate a pathomics score), and spectral computed tomography enterography derived radiomics features (used to calculate a radiomics score) were extracted. We developed single-modality and combined models and evaluated the performance of 14 models using receiver operating characteristic curve analysis.
RESULTS
Among 269 patients with CD, 43.1% of those with ileal CD and 33.5% of those with ileocolonic CD achieved MH after receiving IFX. Baseline albumin and fecal calprotectin were identified as independent predictors (both P < 0.05). The integrated radiomics-pathomics model attained the highest area under the curve: 0.915 in the ileal test cohort and 0.922 in the ileocolonic test cohort, which was significantly superior to the clinical or single modality models (DeLong test, all P < 0.05). Calibration, decision curve analysis, and clinical impact curve verified favorable fitness and clinical applicability.
CONCLUSION
A baseline radiomics-pathomics approach can noninvasively predict MH with high accuracy. This strategy provides complementary pathophysiological information for stratifying treatment response.
Core Tip: In response to the clinical requirement for an accurate, noninvasive predictive instrument for mucosal healing (MH) in Crohn’s disease (CD), this research endeavors to develop and validate a multimodal integrated model. The combined radiomics-pathomics model forecasts MH in ileal/ileocolonic CD, with area under the curve values of 0.915/0.922, surpassing those of other models. It offers a noninvasive, precise means for predicting MH in CD patients, thereby promoting personalized clinical decision-making and enhancing patient prognoses.
