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. 2025 Jun 19.
doi: 10.1007/s00330-025-11746-3. Online ahead of print.

Concordance between single-slice abdominal computed tomography-based and bioelectrical impedance-based analysis of body composition in a prospective study

Uli Fehrenbach #  1 , Clarissa Hosse #  2 , William Wienbrandt  2 , Thula Walter-Rittel  2 , Johannes Kolck  2   3 , Timo Alexander Auer  2   3 , Elisabeth Blüthner  4 , Frank Tacke  4 , Nick Lasse Beetz #  2   3 , Dominik Geisel #  2
Affiliations

Concordance between single-slice abdominal computed tomography-based and bioelectrical impedance-based analysis of body composition in a prospective study

Uli Fehrenbach et al. Eur Radiol. .

Abstract

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.

Keywords: Artificial intelligence; Bioelectrical impedance; Body composition; Computed tomography; Frailty.

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Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Prof. Dr. med. Dominik Geisel. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: One of the authors has significant statistical expertise. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: There was no cohort overlap. Methodology: Prospective Diagnostic or prognostic study/observational Performed at one institution

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