Pan XL, Li HJ, Li Z, Li ZL. Prognostic value of computed tomography derived skeletal muscle mass index in lung cancer: A meta-analysis. World J Clin Cases 2022; 10(20): 6927-6935 [PMID: 36051119 DOI: 10.12998/wjcc.v10.i20.6927]
Corresponding Author of This Article
Zhen-Lin Li, MD, Professor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxuexiang, Chengdu 610041, Sichuan Province, China. 17380096151@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Meta-Analysis
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/
World J Clin Cases. Jul 16, 2022; 10(20): 6927-6935 Published online Jul 16, 2022. doi: 10.12998/wjcc.v10.i20.6927
Prognostic value of computed tomography derived skeletal muscle mass index in lung cancer: A meta-analysis
Xue-Lin Pan, Hong-Jun Li, Zhen Li, Zhen-Lin Li
Xue-Lin Pan, Zhen Li, Zhen-Lin Li, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Hong-Jun Li, West China Hospital of Medicine, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Li ZL made substantial contributions to the conception and design of the work; Pan XL and Li HJ searched and selected the materials and extracted the data; Pan XL wrote the manuscript; Pan XL, Li HJ, Li Z, and Li ZL revised the paper carefully and also contributed to the statistical analysis; all authors have read and approved the final manuscript.
Supported by1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University, No. ZYGD18019.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Zhen-Lin Li, MD, Professor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxuexiang, Chengdu 610041, Sichuan Province, China. 17380096151@163.com
Received: December 5, 2021 Peer-review started: December 5, 2021 First decision: January 25, 2022 Revised: January 26, 2022 Accepted: May 22, 2022 Article in press: May 22, 2022 Published online: July 16, 2022
ARTICLE HIGHLIGHTS
Research background
The prognostic role of the skeletal muscle mass index (SMI) calculated through computed tomography (CT) images in several types of cancers has been demonstrated.
Research motivation
Whether the SMI could serve as a reliable and valuable predictor for long-term survival in lung cancer remains unclear.
Research objectives
To verify the prognostic value of the CT-derived SMI in lung cancer patients.
Research methods
Several electronic databases were searched up to November 5, 2021 for relevant studies. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the association of the SMI with the overall survival (OS) of lung cancer patients. All statistical analyses were performed with STATA 12.0 software.
Research results
The pooled results demonstrated that a lower SMI was significantly related to poorer OS (HR = 1.23, 95%CI: 1.11-1.37, P < 0.001). In addition, the subgroup analyses stratified by treatment (nonsurgery vs surgery), tumor stage (advanced stage vs early stage), and tumor type (non-small cell lung cancer vs lung cancer) showed similar results.
Research conclusions
The CT-derived SMI is a novel and valuable prognostic indicator in lung cancer.
Research perspectives
The SMI might contribute to the clinical management and treatment of lung cancer patients.
