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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence
Zi-Chen Luo, Hai-Yang Guo, Xiao Tang, Xin-Rui Chen, Cheng-Yu Zhang, Yu-Tong Cui, Ji Zuo, Hao-Rui Li, Xue-Mei Hou, Hao Chen, Shao-Bi Song, Xian-Fei Wang
Zi-Chen Luo, Hai-Yang Guo, Xin-Rui Chen, Cheng-Yu Zhang, Yu-Tong Cui, Ji Zuo, Hao-Rui Li, Xue-Mei Hou, Hao Chen, Shao-Bi Song, Xian-Fei Wang, Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Xiao Tang, Department of Gastroenterology, Langzhong People’s Hospital, Langzhong 637400, Sichuan Province, China
Xian-Fei Wang, Department of Gastroenterology, Sichuan Branch of National Clinical Research Center for Digestive Diseases, Nanchong 637000, Sichuan Province, China
Co-first authors: Zi-Chen Luo and Hai-Yang Guo.
Author contributions: Luo ZC and Guo HY contributed equally to this work as co-first authors; Wang XF, Luo ZC, and Guo HY designed the study, analyzed the data, drafted the article, and critically revised the article; Luo ZC, Zhang CY, Guo HY, Chen XR, Cui YT, Zuo J, Li HR, Hou XM, Chen H, Song SB, and Tang X collected the data. All authors have read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of The Affiliated Hospital of North Sichuan Medical College, No. 2024ER140-1.
Informed consent statement: The requirement for informed consent was waived by the Ethics Committee due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original datasets presented in the study are included in the article, further inquiries can be directed to the corresponding author.
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: Xian-Fei Wang, Chief Physician, Full Professor, Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong 637000, Sichuan Province, China.
wangxianfei@nsmc.edu.cn
Received: September 29, 2025
Revised: October 31, 2025
Accepted: December 19, 2025
Published online: February 15, 2026
Processing time: 127 Days and 21.7 Hours
BACKGROUND
Endoscopic submucosal dissection (ESD) serves as a critical treatment modality for superficial esophageal cancer. However, non-curative resection is significantly associated with residual tumors and unfavorable prognosis. Effective preoperative predictive tools are currently lacking.
AIM
To develop and validate a machine learning-based prediction model for accurate preoperative assessment of the risk of non-curative ESD resection.
METHODS
This multicenter retrospective study included 366 superficial esophageal cancer patients from the Affiliated Hospital of North Sichuan Medical College as a training set, and 129 patients from Langzhong People’s Hospital as an independent external validation set. Predictors were selected using least absolute shrinkage and selection operator and multivariate logistic regression. Nine machine learning classifiers, including logistic regression, LightGBM, and XGBoost, were integrated to develop the models, and SHapley Additive exPlanations (SHAP) were employed to achieve risk visualization.
RESULTS
Key predictive factors identified included esophageal stricture, computed tomography-based esophageal wall thickening > 7 mm, endoscopically estimated invasion depth > superficial layer (SM1) (endoscopic ultrasound or magnifying endoscopy with narrow-band imaging collectively referred to as EOM > SM1), multiple lesions, circumferential ratio ≥ 3/4, and preoperative pathological type. The logistic regression model constructed with these factors demonstrated optimal performance (training set area under the curve (AUC) = 0.887; internal validation AUC = 0.872; external validation AUC = 0.849). SHAP analysis further revealed computed tomography-based esophageal wall thickening > 7 mm and EOM > SM1 as core risk-driving factors.
CONCLUSION
The logistic regression prediction model developed in this study effectively identifies patients at high risk of non-curative resection prior to ESD. By incorporating SHAP-based interpretability, the model provides a reliable and transparent tool to support clinical decision-making.
Core Tip: This multicenter study produced an online, interpretable prediction tool that quantifies the preoperative risk of non-curative endoscopic submucosal dissection for superficial esophageal cancer. With a clear cutoff (SHapley Additive exPlanations value ≥ 0.185), it provides immediate, transparent guidance: High-risk patients are directed to radical surgery, while low-risk ones are confirmed as endoscopic submucosal dissection candidates, ensuring the first treatment choice is optimal.
