Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. May 6, 2024; 12(13): 2182-2193
Published online May 6, 2024. doi: 10.12998/wjcc.v12.i13.2182
Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases
Zheng-Yao Chang, Wen-Xing Gao, Yue Zhang, Wen Zhao, Di Wu, Lin Chen
Zheng-Yao Chang, Wen-Xing Gao, Di Wu, Lin Chen, Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
Yue Zhang, Department of Endocrinology, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
Wen Zhao, School of Medicine, Nankai University, Tianjin 300071, China
Co-first authors: Zheng-Yao Chang and Wen-Xin Gao.
Co-corresponding authors: Di Wu and Lin Chen.
Author contributions: Chang ZY and Gao WX contributed to the research design and manuscript writing; Zhang Y and Zhao W contributed to the data collection and analysis; Wu D and Chen L overall supervise the study. All authors have read and approve the submitted version.
Institutional review board statement: The study was reviewed and approved for publication by the Ethics Committee of the Chinese PLA General Hospital.
Informed consent statement: The study was reviewed by the Ethics Committee of the General Hospital of the Chinese People's Liberation Army to review and waive 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: Lin Chen, MD, PhD, Chief, Chief Doctor, Professor, Surgeon, Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. chenlinbj@sina.com
Received: February 1, 2024
Peer-review started: February 1, 2024
First decision: February 28, 2024
Revised: March 8, 2024
Accepted: March 28, 2024
Article in press: March 28, 2024
Published online: May 6, 2024
Processing time: 83 Days and 18.4 Hours
Abstract
BACKGROUND

Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes.

AIM

To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment.

METHODS

Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort.

RESULTS

A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit.

CONCLUSION

Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Keywords: Gastric cancer; Liver metastases; Nomogram; Prognostic model; Survival analysis

Core Tip: This study identifies pivotal prognostic factors and introduces a nomogram model for predicting individualized prognosis in gastric cancer liver metastases (GCLM). The developed model, supported by comprehensive validation, showcases substantial potential for improving patient outcome evaluation. Notably, the incorporation of five independent risk factors demonstrates promising predictive accuracy, paving the way for enhanced clinical decision-making in managing GCLM patients, ultimately offering valuable insights for personalized treatment strategies.