Hijazi Waseem, Shanbhag Aakash, Miller Robert J H, Kavanagh Paul, Killekar Aditya, Lemley Mark, Wopperer Samuel, Knight Stacey, Le Viet T, Mason Steve, Acampa Wanda, Rosamond Thomas, Dey Damini, Berman Daniel S, Chareonthaitawee Panithaya, Di Carli Marcelo F, Slomka Piotr J
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (W.H., A.S., R.J.H.M., P.B.K., A.K., M.L., D.D., D.S.B., P.J.S.).
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles (A.S.).
Circ Cardiovasc Imaging. 2025 Jul;18(7):e018188. doi: 10.1161/CIRCIMAGING.124.018188. Epub 2025 May 13.
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.
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.
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.
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.
计算机断层扫描（CT）衰减校正扫描是使用PET/CT进行正电子发射断层扫描（PET）心肌灌注成像的固有组成部分，但这些超低剂量CT扫描很少能提供解剖学信息。我们旨在评估通过深度学习从这些扫描中得出的心脏腔室容积（右心房、右心室、左心室和左心房）和质量（左心室）与心肌血流储备和心力衰竭住院之间的关联。
我们纳入了来自6个地点的18079例接受心脏PET/CT检查的患者。一个深度学习模型从CT衰减校正成像中估计心脏腔室容积和左心室质量。在多变量分析中评估了通过深度学习得出的CT质量和容积与心力衰竭住院和心肌血流储备降低之间的关联。
在中位随访4.3年期间，1721例（9.5%）患者发生心力衰竭住院。与腔室容积正常的患者相比，3个或4个腔室容积异常的患者因心力衰竭住院的可能性高出7倍。在调整分析中，左心房容积（风险比[HR]，1.25[95%置信区间，1.19 - 1.30]）、右心房容积（HR，1.29[95%置信区间，1.23 - 1.35]）、右心室容积（HR，1.25[95%置信区间，1.20 - 1.31]）、左心室容积（HR，1.27[95%置信区间，1.23 - 1.35]）和左心室质量（HR，1.25[95%置信区间，1.18 - 1.32]）均与心力衰竭住院独立相关。在多变量分析中，左心房容积（优势比，1.14[95%置信区间，1.0 - 1.19]）和心室质量（优势比，1.12[95%置信区间，1.6 - 1.17]）是心肌血流储备降低的独立预测因素。
通过深度学习从CT衰减校正中得出的腔室容积和左心室质量可预测接受心脏PET灌注成像患者的心力衰竭住院情况和心肌血流储备降低。这些解剖学数据可与其他PET/CT参数一起常规报告，以改善风险预测。
