Journal Article

DKFZ-2026-02638

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png AI-based automated body composition analysis from whole-body MRI and (PET-) CT: an intermodal comparison from same-day examinations.

Holzschuh, J. (First author)DKFZ* ; Rotkopf, L. T.DKFZ* ; Neelsen, C.DKFZ* ; Bauer, F. ; Zhang, K. S.DKFZ* ; Menis, E. ; Hajiyianni, M. ; Mai, E. K. ; Weinhold, N. ; John, L. ; Sachpekidis, C.DKFZ* ; Dimitrakopoulou-Strauss, A.DKFZ* ; Raab, M.-S. ; Goldschmidt, H.DKFZ* ; Jende, J.DKFZ* ; Delorme, S.DKFZ* ; Schlemmer, H.-P.DKFZ* ; Wennmann, M. (Last author)DKFZ*

2026
BioMed Central London

Abstract: To determine intermodal agreement of AI-derived volumetric body composition metrics between [18 F]FDG-PET/CT-derived low-dose CT and whole-body MRI (wb-MRI).We retrospectively analyzed same-day whole-body low-dose CT, obtained from [18F]FDG-PET/CT, and wb-MRI from consecutive multiple myeloma patients from the prospective GMMG-HD7- and GMMG-HD8-/DSMM-XIX-trials. An automated deep learning pipeline based on the TotalSegmentator model, and its MRI variant were used to segment skeletal muscle (SM), subcutaneous- (SAT) and abdominal visceral adipose tissue (VAT) from CT and MRI, respectively. Volumetric measures and single-slice estimates were derived. Agreement between CT- and MRI-based metrics was assessed using Pearson r, intraclass correlation coefficients (ICC), and Bland-Altman analyses.Forty-two multiple myeloma patients with same-day PET/CT and wb-MRI were included. For AI-based volumetric body composition metrics, CT and MRI demonstrated high agreement (ICC > 0.95) across all SM, SAT and VAT. However, Bland-Altmann plots revealed systematic modality-related differences between MRI and CT: both SM (bias: -2582 ml, p < 0.0001) and VAT (bias: -211 ml, p < 0.0001) were significantly lower when extracted from MRI compared to CT, while for SAT, the difference was not statistically significant (bias: -263 ml, p = 0.1629). For AI-based single slice analysis, agreement remained high for SAT and VAT (ICC > 0.97), while being lower for SM (ICC 0.87). Bland-Altman plots showed that SM was systematically and significantly lower when obtained from MRI than from CT (bias: -18.4cm2, p < 0.0001).Automated body composition measures from (PET-)CT and wb-MRI are highly concordant but show consistent intermodal biases that should be considered when pooling multimodal data or applying modality-agnostic thresholds.Not applicable.

Keyword(s): Humans (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Positron Emission Tomography Computed Tomography: methods (MeSH) ; Whole Body Imaging: methods (MeSH) ; Male (MeSH) ; Female (MeSH) ; Body Composition (MeSH) ; Multiple Myeloma: diagnostic imaging (MeSH) ; Retrospective Studies (MeSH) ; Aged (MeSH) ; Middle Aged (MeSH) ; Fluorodeoxyglucose F18 (MeSH) ; AI ; Deep Learning ; MRI ; Multiple myeloma ; Oncology ; PET/CT ; Fluorodeoxyglucose F18

Note: #EA:E010#LA:E010# / #NCTZFB26#

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Contributing Institute(s):

1. Radiologie (E010)
2. KKE Nuklearmedizin (E060)
3. Koordinierungsstelle NCT Heidelberg (HD02)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2026

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Database coverage:
; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Essential Science Indicators ; Fees ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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