Prospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2025; 17(1): 96686
Published online Jan 15, 2025. doi: 10.4251/wjgo.v17.i1.96686
Development of a nomogram for overall survival in patients with esophageal carcinoma: A prospective cohort study in China
Shi-Shi Yu, Xi Zheng, Xiao-Sheng Li, Qian-Jie Xu, Wei Zhang, Zhong-Li Liao, Hai-Ke Lei
Shi-Shi Yu, Xi Zheng, Zhong-Li Liao, Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing 400030, China
Xiao-Sheng Li, Wei Zhang, Hai-Ke Lei, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China
Qian-Jie Xu, Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Co-first authors: Shi-Shi Yu and Xi Zheng.
Co-corresponding authors: Zhong-Li Liao and Hai-Ke Lei.
Author contributions: Yu SS conceived and designed the study; Zheng X wrote initial drafts of the paper; Li XS handled the data collection and statistical analysis; Xu QJ, Zhang W, performed analysis and interpretation of statistics; Liao ZL and Lei HK designed the study, revised the article and final approval of the version to be published. All authors collectively designed the methods and experiments, read, and approved the final manuscript. Yu SS and Zheng X contributed equally to this work as co-first authors. Liao ZL works at Department of Gastroenterology, Chongqing University Cancer Hospital and found there was need for a more scientifically robust, systematic, and practical model that incorporates clinically significant indicators to support decision-making. Lei HK works at Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital and is mainly responsible for follow-up of cancer patients. For this reason, Liao ZL and Lei HK designed the study, revised the article and final approval of the version to be published. They are designated as co-corresponding authors.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Chongqing University Tumor Hospital (Approval No. CZLS2023338-A).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: We 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, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Received: May 13, 2024
Revised: September 2, 2024
Accepted: September 9, 2024
Published online: January 15, 2025
Processing time: 213 Days and 2.1 Hours
Abstract
BACKGROUND

Esophageal carcinoma (EC) presents a significant public health issue in China, with its prognosis impacted by myriad factors. The creation of a reliable prognostic model for the overall survival (OS) of EC patients promises to greatly advance the customization of treatment approaches.

AIM

To create a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.

METHODS

This study utilized data from a prospective longitudinal cohort of 3127 EC patients treated at Chongqing University Cancer Hospital between January 1, 2018, and December 12, 2020. Utilizing the least absolute shrinkage and selection operator regression alongside multivariate Cox regression analyses helped pinpoint pertinent variables for constructing the model. Its efficacy was assessed by concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

Nine variables were determined to be significant predictors of OS in EC patients: Body mass index (BMI), Karnofsky performance status, TNM stage, surgery, radiotherapy, chemotherapy, immunotherapy, platelet-to-lymphocyte ratio, and albumin-to-globulin ratio (ALB/GLB). The model demonstrated a C-index of 0.715 (95%CI: 0.701-0.729) in the training cohort and 0.711 (95%CI: 0.689-0.732) in the validation cohort. In the training cohort, AUCs for 1-year, 3-year, and 5-year OS predictions were 0.773, 0.787, and 0.750, respectively; in the validation cohort, they were 0.772, 0.768, and 0.723, respectively, illustrating the model's precision. Calibration curves and DCA verified the model's predictive accuracy and net benefit.

CONCLUSION

A novel prognostic model for determining the OS of EC patients was successfully developed and validated to help clinicians in devising individualized treatment schemes for EC patients.

Keywords: Esophageal carcinoma; High-risk factors; Prognosis; Overall survival; Prediction model

Core Tip: In this study, we identified nine key independent risk factors associated with esophageal carcinoma patients. These factors span clinical characteristics (body mass index, Karnofsky performance status), the TNM stage, treatment approaches (surgery, radiotherapy, chemotherapy, and immunotherapy), and laboratory markers (platelet-to-lymphocyte ratio, albumin-to-globulin ratio). And then, a novel prognostic model was successfully developed and validated. It could be considered as a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.