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Updated: Sep 19, 2025
Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass
Published on: July 14, 2023
Concordance between single-slice abdominal computed tomography-based and bioelectrical impedance-based analysis of
Uli Fehrenbach1, Clarissa Hosse2, William Wienbrandt2
1Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany. uli.fehrenbach@charite.de.
Objectives:
Body composition analysis (BCA) is a recognized indicator of patient frailty. Apart from the established bioelectrical impedance analysis (BIA), computed tomography (CT)-derived BCA is being increasingly explored. The aim of this prospective study was to directly compare BCA obtained from BIA and CT.
Materials And Methods:
A total of 210 consecutive patients scheduled for CT, including a high proportion of cancer patients, were prospectively enrolled. Immediately prior to the CT scan, all patients underwent BIA. CT-based BCA was performed using a single-slice AI tool for automated detection and segmentation at the level of the third lumbar vertebra (L3). BIA-based parameters, body fat mass (BFMBIA) and skeletal muscle mass (SMMBIA), CT-based parameters, subcutaneous and visceral adipose tissue area (SATACT and VATACT) and total abdominal muscle area (TAMACT) were determined. Indices were calculated by normalizing the BIA and CT parameters to patient's weight (body fat percentage (BFPBIA) and body fat index (BFICT)) or height (skeletal muscle index (SMIBIA) and lumbar skeletal muscle index (LSMICT)).
Results:
Parameters representing fat, BFMBIA and SATACT + VATACT, and parameters representing muscle tissue, SMMBIA and TAMACT, showed strong correlations in female (fat: r = 0.95; muscle: r = 0.72; p < 0.001) and male (fat: r = 0.91; muscle: r = 0.71; p < 0.001) patients. Linear regression analysis was statistically significant (fat: R2 = 0.73 (female) and 0.74 (male); muscle: R2 = 0.56 (female) and 0.56 (male); p < 0.001), showing that BFICT and LSMICT allowed prediction of BFPBIA and SMIBIA for both sexes.
Conclusion:
CT-based BCA strongly correlates with BIA results and yields quantitative results for BFP and SMI comparable to the existing gold standard.
Key Points:
Question CT-based body composition analysis (BCA) is moving more and more into clinical focus, but validation against established methods is lacking. Findings Fully automated CT-based BCA correlates very strongly with guideline-accepted bioelectrical impedance analysis (BIA). Clinical relevance BCA is currently moving further into clinical focus to improve assessment of patient frailty and individualize therapies accordingly. Comparability with established BIA strengthens the value of CT-based BCA and supports its translation into clinical routine.
