ArticleTranslational pediatrics2026
Comparing muscle mass in children with high-risk neuroblastoma using magnetic resonance imaging and bioelectrical impedance analysis.
Article in Translational pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Evidence from recent years suggests that bioelectrical impedance analysis (BIA), or BIA-based assessments of muscle mass, are strongly correlated with magnetic resonance imaging (MRI)-based assessments of muscle mass. Nevertheless, no research has examined how the two approaches relate to kids with high-risk neuroblastoma (HR-NBL). This study examined the clinical relevance of BIA-based and MRI-based muscle mass assessment. Before surgery, we computed the relationship between muscle mass measurements made using BIA and MRI in HR-NBL patients. Methods: We retrospectively collected data for patients aged 3 to 18 years who were newly diagnosed with HR-NBL at the Children's Hospital, Zhejiang University School of Medicine from November 2023 to November 2024. L4 lumbar levels were identified on axial MRI images, and we measured skeletal muscle cross-sectional area. The Multi-Frequency Body Composition Analyzer InBody S10 (Biospace Co., Ltd., Seoul, Korea) was used to estimate skeletal muscle mass (SMM). The analysis included 32 children. Results: The median patient age was 4.8 years (range, 4.03-6.2 years); 56.3% of the patients were boys, and 43.7% were girls. SMM measured by BIA showed a strong positive correlation with MRI-measured SMM in patients (r=0.956, P<0.001). The concordance correlation coefficient for SMM was 0.862, with a 95% confidence interval (CI) of 0.799-0.905. The SMM showed a mean bias of 0.319±1.326 kg, indicating that the BIA method overestimated SMM by 0.319 kg compared to MRI. The Bland-Altman analysis suggests that most participants were within the limits of agreement (LoA). Multiple linear regression analysis revealed that weight was a significant predictor of the outcome variable in the models tested. In the multiple regression model, weight (estimate =0.229, P<0.001) was significantly associated with the outcome, and the model explained 78.8% of the variance (R2=0.788). Conclusions: In conclusion, in monitoring muscle mass in pediatric HR-NBL patients, BIA measurements showed good correlation and agreement with MRI measurements. The multiple linear regression analysis shows that weight is a significant predictor of the difference in muscle mass in human body composition.
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