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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Prognostic value of modified Lauren classification in gastric cancer
Fei-Long Ning, Nan-Nan Zhang, Jun Wang, Yi-Feng Jin, Hong-Guang Quan, Jun-Peng Pei, Yan Zhao, Xian-Tao Zeng, Masanobu Abe, Chun-Dong Zhang
Fei-Long Ning, Jun Wang, Hong-Guang Quan, Department of General Surgery, Xuzhou Hospital of Traditional Chinese Medicine, Xuzhou 221000, Jiangsu Province, China
Nan-Nan Zhang, State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, The Fourth Military Medical University, Xi’an 710000, Shannxi Province, China
Yi-Feng Jin, Department of General Surgery, Jiading Hospital of Traditional Chinese Medicine, Shanghai 201800, China
Jun-Peng Pei, Chun-Dong Zhang, Department of Gastrointestinal Surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
Yan Zhao, Department of Stomach Surgery, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang 110042, Liaoning Province, China
Xian-Tao Zeng, Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, Hubei Province, China
Xian-Tao Zeng, Department of Evidence-Based Medicine and Clinical Epidemiology, The Second Clinical College of Wuhan University, Wuhan 430071, Hubei Province, China
Masanobu Abe, Division for Health Service Promotion, The University of Tokyo, Tokyo 113-0033, Japan
Chun-Dong Zhang, Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
Author contributions: Ning FL, Zhang NN, Wang J, and Zhang CD contributed to the concept and design of the study; Ning FL, Zhang NN, and Zhang CD contributed to the acquisition of the data; Ning FL, Zhang NN, Wang J, Zeng XT, and Zhang CD contributed to the analysis and interpretation of the data; Ning FL, Zhang NN, Wang J, Jin YF, Quan HG, Pei JP, Zhao Y, Zeng XT, Abe M, and Zhang CD contributed to the drafting of the manuscript; Ning FL, Zhang CD contributed to the critical revision of the manuscript for important intellectual content; Zhang CD contributed to obtaining the funding.
Supported by The China Scholarship Council, No. 201908050148.
Institutional review board statement: This study was approved by the Institutional Review Board of The Fourth Affiliated Hospital of China Medical University (EC-2021-KS-047).
Informed consent statement: Informed consent statement was waived by the Institutional Review Board of The Fourth Affiliated Hospital of China Medical University.
Conflict-of-interest statement: All authors declare no potential conflicts of interest for this manuscript.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the SEER database.
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:
http://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Chun-Dong Zhang, MD, Lecturer, Department of Gastrointestinal Surgery, The Fourth Affiliated Hospital of China Medical University, No. 4 Chongshan East Road, Huanggu District, Shenyang 110032, Liaoning Province, China. cdzhang@cmu.edu.cn
Received: April 12, 2021
Peer-review started: April 12, 2021
First decision: June 23, 2021
Revised: July 1, 2021
Accepted: July 27, 2021
Article in press: July 27, 2021
Published online: September 15, 2021
BACKGROUND
It remains controversial as to which pathological classification is most valuable in predicting the overall survival (OS) of patients with gastric cancer (GC).
AIM
To assess the prognostic performances of three pathological classifications in GC and develop a novel prognostic nomogram for individually predicting OS.
METHODS
Patients were identified from the Surveillance, Epidemiology, and End Results program. Univariate and multivariate analyses were performed to identify the independent prognostic factors. Model discrimination and model fitting were evaluated by receiver operating characteristic curves and Akaike information criteria. Decision curve analysis was performed to assess clinical usefulness. The independent prognostic factors identified by multivariate analysis were further applied to develop a novel prognostic nomogram.
RESULTS
A total of 2718 eligible GC patients were identified. The modified Lauren classification was identified as one of the independent prognostic factors for OS. It showed superior model discriminative ability and model-fitting performance over the other pathological classifications, and similar results were obtained in various patient settings. In addition, it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3- and 5-year OS. A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability, model-fitting performance, and net benefits over the American Joint Committee on Cancer 8th edition tumor-node-metastasis classification.
CONCLUSION
The modified Lauren classification shows superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS. A novel prognostic nomogram incorporating the modified Lauren classification shows good model discriminative ability, model-fitting performance, and net benefits.
Core Tip: In this study, we compared the prognostic performances among the modified Lauren classification, the Lauren classification, and tumor differentiation grade. The modified Lauren classification was identified as one of the independent prognostic factors for overall survival. It showed superior model discriminative ability, model-fitting performance, and net benefits over the other classifications. We further developed a novel prognostic nomogram of individually predicting overall survival by incorporating the modified Lauren classification.
