Deep learning-derived abnormal cardiac chamber volumes from PET/CT attenuation scans were associated with a 7-fold higher risk of heart failure hospitalization compared with normal volumes.
Cohort (n=18,079)
Yes
Do deep learning-derived cardiac chamber volumes and mass from PET/CT attenuation scans predict heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging?
Deep learning can extract prognostic anatomic data (chamber volumes and mass) from routine ultralow-dose CT attenuation correction scans during PET/CT, improving risk prediction for heart failure.
Effect estimate: HR 1.25 (95% CI 1.19-1.30)
BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization. METHODS: We included 18 079 patients with cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis. RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio HR, 1.25 95% CI, 1.19-1.30), right atrial volume (HR, 1.29 95% CI, 1.23-1.35), right ventricular volume (HR, 1.25 95% CI, 1.20-1.31), left ventricular volume (HR, 1.27 95% CI, 1.23-1.35), and left ventricular mass (HR, 1.25 95% CI, 1.18-1.32) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 95% CI, 1.0-1.19) and ventricular mass (odds ratio, 1.12 95% CI, 1.6-1.17) were independent predictors of reduced myocardial flow reserve. CONCLUSIONS: Deep learning-derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.
Hijazi et al. (Tue,) conducted a cohort in Patients undergoing cardiac PET perfusion imaging (n=18,079). Deep learning-derived cardiac chamber volumes and mass from CT attenuation correction scans vs. Normal chamber volumes was evaluated on Heart failure hospitalization (HR 1.25, 95% CI 1.19-1.30). Deep learning-derived abnormal cardiac chamber volumes from PET/CT attenuation scans were associated with a 7-fold higher risk of heart failure hospitalization compared with normal volumes.