Lyu KM, Chen QQ, Xu YF, Yuan YQ, Wang JF, Wan J, Ling-Hu EQ. Development and validation of a predictive model for the pathological upgrading of gastric low-grade intraepithelial neoplasia. World J Gastroenterol 2025; 31(11): 104377 [PMID: 40124264 DOI: 10.3748/wjg.v31.i11.104377]
Corresponding Author of This Article
En-Qiang Ling-Hu, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, The First Medical Center of Chinese People’s Liberation Army General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. linghuenqiang@vip.sina.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Kun-Ming Lyu, Jun Wan, Department of Gastroenterology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
Kun-Ming Lyu, Yao-Qian Yuan, Medical College, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
Qian-Qian Chen, Yao-Qian Yuan, Jia-Feng Wang, En-Qiang Ling-Hu, Department of Gastroenterology, The First Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100853, China
Yi-Fan Xu, Department of Gastroenterology, General Hospital of Central Theater Command, Wuhan 430000, Hubei Province, China
Co-first authors: Kun-Ming Lyu and Qian-Qian Chen.
Co-corresponding authors: Jun Wan and En-Qiang Ling-Hu.
Author contributions: Ling-Hu EQ, Wan J, and Chen QQ designed the research; Lyu KM and Yuan YQ played key roles in data collection; Lyu KM and Chen QQ contributed equally as co-first authors; Xu YF and Wang JF provided statistical expertise and editorial assistance; Lyu KM wrote the manuscript; Ling-Hu EQ and Wan J oversaw the project and contributed equally as co-corresponding authors; and all authors participated in a thorough review and refinement of the manuscript and approved its final version for publication.
Supported by the National Key Research and Development Program of China, No. 2022YFC2503600.
Institutional review board statement: The study protocol was reviewed and approved by the Ethics Committee of the Chinese People’s Liberation Army General Hospital (Ethics audits No. S2023-188-01).
Informed consent statement: Patients were not required to provide informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to MR surgery by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement:
All data obtained or analyzed during this work are included within the article.
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: En-Qiang Ling-Hu, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, The First Medical Center of Chinese People’s Liberation Army General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. linghuenqiang@vip.sina.com
Received: December 20, 2024 Revised: January 17, 2025 Accepted: February 11, 2025 Published online: March 21, 2025 Processing time: 83 Days and 2.7 Hours
Abstract
BACKGROUND
The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia (LGIN) presents challenges in developing diagnostic and treatment protocols.
AIM
To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.
METHODS
We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023. A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.
RESULTS
A total of 171 patients were included in this study: 93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN. The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800, respectively, while the least absolute shrinkage and selection operator (LASSO) regression model showed sensitivity and specificity values of 0.842 and 0.840, respectively. The area under the curve (AUC) for the logistic model was 0.896, slightly lower than the AUC of 0.904 for the LASSO model. Internal validation with 30% of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model. The LASSO model provided greater utility in clinical decision-making.
CONCLUSION
A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
Core Tip: This study developed and validated a predictive model for the pathological upgrading of gastric low-grade intraepithelial neoplasia using clinical and endoscopic characteristics. The least absolute shrinkage and selection operator (LASSO) regression analysis identified nine key predictors from 30 variables related to pathological upgrading. The LASSO model demonstrated an area under the curve of 0.904, and internal validation with 30% of the data yielded an area under the curve of 0.905, confirming its clinical utility.
