Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2025; 31(13): 104466
Published online Apr 7, 2025. doi: 10.3748/wjg.v31.i13.104466
Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study
Yong-Le Zhang, Hai-Bin Song, Ying-Wei Xue
Yong-Le Zhang, Hai-Bin Song, Ying-Wei Xue, Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
Co-corresponding authors: Hai-Bin Song and Ying-Wei Xue.
Author contributions: Zhang YL provided the idea for the article and completed the writing of the main manuscripts; Zhang YL was involved in data collection and statistical analysis; Xue YW and Song HB participated in the revision of the manuscript and approved the final manuscript. All authors contributed to the article and approved the submitted version. The decision to designate two individuals as co-corresponding authors is based on their significant and complementary contributions to the research and manuscript preparation process. Dr. Xue YW has taken the lead in overseeing the overall direction of the study, ensuring the integrity and coherence of the manuscript, and supervising the entire research process, including the acquisition of necessary resources. On the other hand, Dr. Song HB has provided crucial support in guiding the research team with statistical analysis, data collection, and revision of the manuscript. Both authors have played essential roles in ensuring the success of the project, making it fitting to credit them both as co-corresponding authors.
Supported by Nn10 Program of Harbin Medical University Cancer Hospital, No. Nn10 PY 2017-03.
Institutional review board statement: This study was approved by the Ethics Committee of Harbin Medical University Cancer Hospital (Ethics Approval No.2018-02-R) and all participants provided written informed consent. The study design and implementation strictly adhered to the Declaration of Helsinki and relevant ethical guidelines.
Informed consent statement: All study participants in the training set, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors have declared that there is no competition or conflict of interest.
Data sharing statement: The data involved in this study can be obtained from 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: Ying-Wei Xue, MD, PhD, Chief Physician, Postdoctoral Fellow, Professor, Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Received: December 23, 2024
Revised: February 26, 2025
Accepted: March 19, 2025
Published online: April 7, 2025
Processing time: 102 Days and 20.2 Hours
Abstract
BACKGROUND

The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic status before treatment and determine appropriate treatment strategies.

AIM

To develop positive lymph node ratio (LNR) and machine learning (ML)-based staging systems for GC patients with varying differentiation.

METHODS

This multicenter retrospective cohort study included 11772 GC patients, with 5612 in the training set (Harbin Medical University Cancer Hospital) and 6160 in the validation set (Surveillance, Epidemiology, and End Results Program database). X-tile software identified optimal cutoff values for the positive LNR, and five ML models were developed using pT and LNR staging. Risk scores were divided into seven stages, constructing new staging systems tailored to different tumor differentiation levels.

RESULTS

In both the training and validation sets, regardless of the tumor differentiation level, LNR staging demonstrated superior prognostic stratification compared to pN. Extreme Gradient Boosting exhibited better predictive performance than the other four models. Compared to tumor node metastasis staging, the new staging systems, developed for patients with different degrees of differentiation, showed significantly better predictive performance.

CONCLUSION

The new positive lymph nodes ratio staging and integrated staging systems constructed for GC patients with different differentiation grades exhibited better prognostic stratification capabilities.

Keywords: Gastric cancer; Machine learning; Positive lymph nodes ratio; Prognostic staging system; Tumor differentiation

Core Tip: This study introduces new machine learning-based gastric cancer (GC) staging systems, which incorporate the positive lymph node ratio and pT stages, tailored for well/moderately differentiated GC and poorly differentiated GC patients, respectively. These novel systems demonstrate superior prognostic accuracy compared to the traditional American Joint Committee on Cancer tumor node metastasis staging system, providing a more precise tool for predicting overall survival in resectable GC patients and guiding personalized treatment strategies.