Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Feb 27, 2024; 16(2): 357-381
Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.357
Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging
Zhi-Ren Chen, Mei-Fang Yang, Zhi-Yuan Xie, Pei-An Wang, Liang Zhang, Ze-Hua Huang, Yao Luo
Zhi-Ren Chen, Department of Science and Education, Xuzhou Medical University, Xuzhou Clinical College, Xuzhou 221000, Jiangsu Province, China
Mei-Fang Yang, Department of Neurology, Xuzhou Medical University, Xuzhou 221000, Jiangsu Province, China
Zhi-Yuan Xie, Department of Neurology, Clinical Laboratory, Gastrointestinal Surgery, Central Hospital of Xuzhou, Central Hospital of Xuzhou, Xuzhou 221000, Jiangsu Province, China
Pei-An Wang, Department of Public Health, Xuzhou Central Hospital, Xuzhou 221000, Jiangsu Province, China
Liang Zhang, Department of Gastroenterology, Xuzhou Centre Hospital, Xuzhou 221000, Jiangsu Province, China
Ze-Hua Huang, Yao Luo, Department of Public Health, Xuzhou Medical University, Xuzhou 221000, Jiangsu Province, China
Co-first authors: Zhi-Ren Chen and Mei-Fang Yang.
Author contributions: Chen RZ and Yang MF contributed equally to this work; Chen RZ wrote a manuscript, Xie ZY conceptualized and designed the study, and Yang MF provided the study materials; Wang PA provided administrative support; Zhang L collected and assembled the data; Luo Y performed data analysis and interpretation; All authors participated in manuscript writing and approved the final manuscript.
Supported by Peng-Cheng Talent-Medical Young Reserve Talent Training Program, No. XWRCHT20220002; Xuzhou City Health and Health Commission Technology Project Contract, No. XWKYHT20230081; and Key Research and Development Plan Project of Xuzhou City, No. KC22179.
Institutional review board statement: The SEER database is a nationwide cancer registry funded by the National Cancer Institute, which operates across multiple centers and populations. It does not undergo medical ethics review and does not necessitate informed consent. The data used in this study is from the United States public database SEER.
Informed consent statement: The SEER database is a multi-center and multi-population registry funded by the National Cancer Institute that is not subject to medical ethics review and does not require informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
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: Pei-An Wang, MD, PhD, Professor, Researcher, Department of Public Health, Xuzhou Central Hospital, No. 15 Building Community, Xuzhou 221000, Jiangsu Province, China. 302303121267@stu.xzhmu.edu.cn
Received: November 3, 2023
Peer-review started: November 3, 2023
First decision: December 6, 2023
Revised: December 16, 2023
Accepted: January 19, 2024
Article in press: January 19, 2024
Published online: February 27, 2024
Processing time: 113 Days and 21.9 Hours
ARTICLE HIGHLIGHTS
Research background

Gastric cancer (GC) is a globally prevalent malignancy known for its aggressive behaviour and poor survival outcomes, especially when metastasis occurs. Recent research has focused on identifying more precise prognostic factors to tailor individual treatment strategies. By developing a nomogram using e Surveillance, epidemiology, and end results program (SEER) database, this study addresses a critical gap in understanding GC lung metastasis (GCLM). This approach goes beyond traditional American Joint Committee on Cancer staging, thus offering a more accurate predictive model for overall survival (OS) and risk categorization in GCLM patients. This contribution is significant because it can inform better clinical decision-making and potentially improve outcomes in this patient population.

Research motivation

This study was motivated by the need to improve prognostic predictions for GCLM, which is a condition associated with notably poor survival outcomes. The aim was to address key problems in current prognostic models, such as their limited ability to accurately predict OS and cumulative incidence prediction (CIP) in GCLM patients. The significance of solving these problems lies in providing clinicians with a more effective tool for risk stratification, which can guide personalized treatment plans and potentially improve patient outcomes. By developing a more accurate and comprehensive nomogram using data from the SEER database, this research contributes to the advancement of precision medicine in GC care, particularly for those with lung metastases.

Research objectives

The primary objective of this study was to develop an accurate prognostic nomogram for patients with GCLM by using data from the SEER database. This nomogram aims to predict OS and CIP more effectively than existing models. The study successfully identified significant prognostic factors related to GCLM, thus integrating these factors into a model that offers more precise survival predictions and risk stratification. These objectives are significant for future research, as they will enhance the understanding of GCLM and aid in the advancement of personalized treatment strategies, thus potentially improving patient outcomes in this challenging cancer subtype.

Research methods

This research utilized a retrospective analysis of data from the SEER database comprising patients with GCLM from January 2000 to December 2020. The methods included univariate and multivariate Cox regression analyses to identify independent prognostic factors, and a nomogram was developed for predicting OS. This nomogram was validated by using the time-dependent area under the curve and calibration curves. Additionally, decision curve analysis was used to assess the clinical usefulness of the model. The novelty of this research lies in the comprehensive approach combining various clinical and demographic variables that have not been previously integrated in traditional models, thereby enhancing the prognostic accuracy for GCLM patients.

Research results

This research established a novel prognostic nomogram for predicting OS in patients with GCLM that included factors such as age, sex, race, tumour size, and treatment modality. This model demonstrated superior predictive accuracy compared to traditional staging systems, thereby significantly contributing to personalized treatment planning and risk assessment in GCLM patients. However, challenges remain in validating the nomogram across diverse patient populations and integrating emerging biomarkers and genetic data for further refinement of the predictive model.

Research conclusions

The research concluded with the successful development of a prognostic nomogram for predicting the OS of patients with lung metastases from GC based on an extensive and precise collection of clinicopathological variables. Moreover, this approach helps to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment. This model, which was validated for its reliability and clinical application, represents a significant innovation in personalized treatment and prognosis strategies for GC.

Research perspectives

Future efforts will focus on additional external validation and prospective evaluations to further establish the model's efficacy and applicability in clinical settings.