Cancer imaging : the official publication of the International Cancer Imaging Society
AI-based automated body composition analysis from whole-body MRI and (PET-) CT: an intermodal comparison from same-day examinations
Par Holzschuh JC, Rotkopf LT, Neelsen C et 15 autres.
Contenu en anglais
Résumé
Background
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).
Methods
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.