Liang GZ, Li XS, Hu ZH, Xu QJ, Wu F, Wu XL, Lei HK. Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma. World J Gastrointest Oncol 2025; 17(2): 95423 [DOI: 10.4251/wjgo.v17.i2.95423]
Corresponding Author of This Article
Hai-Ke Lei, Doctor, Associate Professor, The Research Center of Big Data, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Guan-Zhong Liang, Xiang-Lin Wu, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China
Xiao-Sheng Li, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China
Zu-Hai Hu, Qian-Jie Xu, Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Fang Wu, Research Center for Medicine and Social Development, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Hai-Ke Lei, The Research Center of Big Data, Chongqing University Cancer Hospital, Chongqing 400030, China
Co-first authors: Guan-Zhong Liang and Xiao-Sheng Li.
Co-corresponding authors: Xiang-Lin Wu and Hai-Ke Lei.
Author contributions: Liang GZ and Li XS contributed equally to this study as co-first authors. Wu XL and Lei HK contributed equally to this study as co-corresponding authors. Liang GZ and Lei HK were responsible for writing the original draft and designing the model; Li XS designed, experimented, and interpreted the model; Hu ZH and Xu QJ analyzed the data; Wu F and Wu XL reviewed and edited the manuscript; all authors collectively designed the methods and experiments, and read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Chongqing University Tumor Hospital (Approval No. CZLS2023343-A).
Clinical trial registration statement: This study did not require clinical registration.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors have no financial relationships to disclose.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
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: Hai-Ke Lei, Doctor, Associate Professor, The Research Center of Big Data, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Received: April 10, 2024 Revised: October 1, 2024 Accepted: November 6, 2024 Published online: February 15, 2025 Processing time: 282 Days and 23.9 Hours
Abstract
BACKGROUND
The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China, with the disease's intricate and varied characteristics further amplifying its health impact. Precise forecasting of overall survival (OS) is of paramount importance for the clinical management of individuals afflicted with this malignancy.
AIM
To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.
METHODS
Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020. Least absolute shrinkage and selection operator, univariate, and multivariate Cox regression analyses were employed to identify independent prognostic factors. A nomogram model was developed to predict gastric cancer patient outcomes. The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves. To evaluate the clinical utility of the model, Kaplan-Meier and decision curve analyses were performed.
RESULTS
A total of ten independent prognostic factors were identified, including body mass index, tumor-node-metastasis (TNM) stage, radiation, chemotherapy, surgery, albumin, globulin, neutrophil count, lactate dehydrogenase, and platelet-to-lymphocyte ratio. The area under the curve (AUC) values for the 1-, 3-, and 5-year survival prediction in the training set were 0.843, 0.850, and 0.821, respectively. The AUC values were 0.864, 0.820, and 0.786 for the 1-, 3-, and 5-year survival prediction in the validation set, respectively. The model exhibited strong discriminative ability, with both the time AUC and time C-index exceeding 0.75. Compared with TNM staging, the model demonstrated superior clinical utility. Ultimately, a nomogram was developed via a web-based interface.
CONCLUSION
This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients, which demonstrated strong predictive ability. Based on these findings, this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
Core Tip: One of the main contributions of this study is that we developed an accessible web-based tool that allows clinicians to easily input patient data and obtain survival prediction, thereby enhancing the model's clinical applicability. This model provides insights that can guide personalized treatment strategies and potentially improve patient outcomes. More importantly, this model demonstrates good predictive ability and accuracy, thus assisting clinicians in creating personalized interventions for patients with gastric cancer.
