Bioelectrical impedance analysis-derived skeletal muscle mass index versus computed tomography for the detection of muscle mass reduction in patients with gastrointestinal cancer: a cross-sectional study

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Authors: Bo Gao, Qinggang Yuan, Hao Zhang, Wenqing Chen, Xiangrui Li, Xiaotian Chen
Cited by: 1
References: 35

Abstract

Background The aims of this study were to assess the precision of bioelectrical impedance analysis (BIA) in evaluating muscle mass and to establish a population-specific cutoff value for identifying muscle mass reduction in a Chinese population with gastric cancer. Methods A total of 163 patients with gastric cancer were enrolled. Skeletal muscle mass was measured at the L3 level using computed tomography (CT) scans. Muscle mass was concurrently evaluated using BIA. The correlations of muscle mass between CT and BIA methods were assessed. Data consistency was analyzed by the intraclass correlation coefficient (ICC). The optimal cutoff value of the BIA-derived skeletal muscle index (SMI) for identifying muscle mass reduction was determined by receiver operating characteristic (ROC) curve analysis. Results The mean skeletal muscle mass measured by CT and BIA was 118.81 ± 24.54 cm 2 and 25.11 ± 4.37 kg in overall patients, respectively. The mean SMI measured by BIA was 9.42 ± 1.09 kg/m 2 . SMI measured by CT was correlated with that measured by BIA ( r = 0.727, p < 0.001). The ICC between the two methods was 0.903, indicating a satisfactory consistency. The optimal BIA-derived SMI cutoff values for identifying muscle mass reduction were 9.46 kg/m² for men and 8.72 kg/m² for women in this population. Conclusions Muscle mass assessed by BIA showed a high correlation and satisfactory consistency with that measured by CT scan.

Fields of Study & Subjects

Nutrition and Health in Aging Stoma care and complications Body Composition Measurement Techniques

Publication details

Journal / source
Frontiers in Oncology
Publisher
Frontiers Media SA
Volume
16
Pages
1769615–1769615
Publication date
23 February 2026
Language
EN