Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jun 15, 2024; 16(6): 2404-2418
Published online Jun 15, 2024. doi: 10.4251/wjgo.v16.i6.2404
Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study
Jie Song, Xiang-Xiu Yan, Fang-Liang Zhang, Yong-Yi Lei, Zi-Yin Ke, Fang Li, Kai Zhang, Yu-Qi He, Wei Li, Chao Li, Yuan-Ming Pan
Jie Song, Xiang-Xiu Yan, Department of Gastroenterology, Dongying People’s Hospital, Dongying Hospital of Shandong Provincial Hospital Group, Dongying 257000, Shandong Province, China
Fang-Liang Zhang, Gastrointestinal Surgery Department, Suining Central Hospital, Suining 629000, Sichuan Province, China
Yong-Yi Lei, Obstetrical Department, Suining Central Hospital, Suining 629000, Sichuan Province, China
Zi-Yin Ke, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, Guangdong Province, China
Fang Li, Department of Pathology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing 100049, China
Kai Zhang, General Department, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
Yu-Qi He, Department of Gastroenterology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
Wei Li, Department of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, Sichuan Province, China
Chao Li, Department of Gastroenterology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine, Beijing 100049, China
Yuan-Ming Pan, Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
Co-first authors: Jie Song and Xiang-Xiu Yan.
Co-corresponding authors: Yuan-Ming Pan and Chao Li.
Author contributions: Song J and Yan XX contributed equally to this study; Song J and Yan XX designed the research study; Li W, Zhang FL, Lei YY, Ke ZY, and Zhang K collected the data; Li F, Zhang K, and He YQ performed statistical analysis; Li F and Li C checked and interpreted endoscopic image; Pan YM and Li W designed the prediction tool; Song J and Yan XX wrote the manuscript; and all authors have read and approve the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Beijing Chest Hospital affiliated to Capital Medical University (Approval No. LW-2024-004).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: The code used in this study is available from the corresponding author upon request. The deep learning-based tool used to predict the OS of patients with gastrointestinal mucous carcinoma after surgery is also available from the corresponding author. The raw data are saved in Supplementary Table 5.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Yuan-Ming Pan, PhD, Cancer Research Center, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing 101149, China. peterfpan2020@mail.ccmu.edu.cn
Received: December 29, 2023
Revised: February 27, 2024
Accepted: April 3, 2024
Published online: June 15, 2024
Processing time: 168 Days and 10.7 Hours
Abstract
BACKGROUND

Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival.

AIM

To investigate the prognosis of GMA and develop predictive model.

METHODS

From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA’s prognosis.

RESULTS

This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors’ use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001).

CONCLUSION

The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Keywords: Deep learning; Gastrointestinal mucous adenocarcinoma; Overall survival; Surgery; Clinical tool

Core Tip: After surgery, some patients can be diagnosed with gastrointestinal mucous adenocarcinoma (GMA) by pathology, a rare subtype cancer. However, research on GMA is limited and controversial, and there is no reference tool for their postoperative survival prediction. We searched Surveillance, Epidemiology, and End Results database and collected 11390 GMA patients’ clinical information. Then we constructed a deep learning-based tool to predict GMA patients’ overall survival after surgery, and the tool has been uploaded. After our analysis, combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.