Prognostic value of computed tomography derived skeletal muscle mass index in lung cancer: A meta-analysis

doi:10.12998/wjcc.v10.i20.6927

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Meta-Analysis

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 by 1·3·5 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.

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
Processing time: 211 Days and 23.1 Hours

Abstract

BACKGROUND

The prognostic role of the skeletal muscle mass index (SMI) derived from computed tomography (CT) imaging been well verified in several types of cancers. However, whether the SMI could serve as a reliable and valuable predictor of long-term survival in lung cancer patients remains unclear.

AIM

To identify the prognostic value of the CT-derived SMI in lung cancer patients.

METHODS

The PubMed, Web of Science, and Embase electronic databases were searched up to November 5, 2021 for relevant studies. The Reference Citation Analysis databases were used during the literature searching and selection. 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.

RESULTS

A total of 12 studies involving 3002 patients were included. 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.

CONCLUSION

The CT-derived SMI is a novel and valuable prognostic indicator in lung cancer and might contribute to the clinical management and treatment of lung cancer patients.

Keywords: Skeletal muscle mass index; Computed tomography; Lung cancer; Prognosis; Meta-analysis

Core Tip: We searched the PubMed, Web of Science, and Embase electronic databases up to November 5, 2021, and a total of 12 studies involving 3002 patients were included. The pooled results demonstrated that a lower skeletal muscle mass index (SMI) was significantly related to poorer overall survival (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. The computed tomography-derived SMI is a novel and valuable prognostic indicator in lung cancer and might contribute to the clinical management and treatment of lung cancer patients.