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
Abstract
BACKGROUND

Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour.

AIM

To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data.

METHODS

Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.

RESULTS

For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI.

CONCLUSION

The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

Keywords: Gastric cancer; Lung metastasis; Nomograms; Surveillance; Epidemiology; Surveillance epidemiology and end results program database; Overall survival; Prognosis

Core Tip: From the viewpoints of overall survival and cancer-specific survival, this study investigated the survival probability based on independent prognostic indicators in gastric cancer with lung metastasis (GCLM) patients and generated a nomogram; the cumulative incidence of disease initiation was predicted. A risk score is assigned to each patient, and vital assistance is offered for individualized treatment plans in GCLM. Moreover, this groundbreaking study provides a model for the prognosis and prevention of various malignancies.