AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.

作者信息

Yi Jirong, Marcinkiewicz Anna M, Shanbhag Aakash, Miller Robert J H, Geers Jolien, Zhang Wenhao, Killekar Aditya, Manral Nipun, Lemley Mark, Buchwald Mikolaj, Kwiecinski Jacek, Zhou Jianhang, Kavanagh Paul B, Liang Joanna X, Builoff Valerie, Ruddy Terrence D, Einstein Andrew J, Feher Attila, Miller Edward J, Sinusas Albert J, Berman Daniel S, Dey Damini, Slomka Piotr J

机构信息

Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center of Radiological Diagnostics, National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland.

出版信息

Lancet Digit Health. 2025 May 12:100862. doi: 10.1016/j.landig.2025.02.002.

DOI:10.1016/j.landig.2025.02.002

PMID:40382274

Abstract

BACKGROUND

CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.

METHODS

We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves.

FINDINGS

The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<0·0001, 1·55, 1·26-1·90; p<0·0001, and 1·30, 1·06-1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62-0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44-0·71; p<0·0001).

INTERPRETATION

CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value.