Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

作者：Anna M. Michalowska, Wenhao Zhang, A. Shanbhag, Robert J. H. Miller, M. Lemley, G. Ramirez, M. Buchwald, A. Killekar, P. Kavanagh, A. Feher, E. Miller, A. Einstein, T. Ruddy, Joanna X. Liang, V. Builoff, David Ouyang, Daniel S. Berman, D. Dey, P. Slomka · 发表于：medRxiv · 年份：2024 · DOI：10.1101/2024.04.23.24305735 · 被引用次数：3 · 研究领域：Medicine

Backgrounds: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), wh...