Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Abstract

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in two positions (semi-upright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semi-upright and supine stress MPI by deep learning (DL) as compared to standard combined total perfusion deficit (TPD). Methods: 1160 patients without known coronary artery disease (64% males) were studied. Patients underwent stress ^99mTc-sestamibi MPI with new generation solid-state SPECT scanners in four different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within six months of MPI. Obstructive disease was defined as ≥70% narrowing of the 3 major coronary arteries and ≥50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using gender- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, four DL models were trained using data from three centers and then evaluated on the one center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1272 of 3480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient: 0.81 vs 0.78, per-vessel: 0.77 vs 0.73, P<0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P<0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P<0.01). With threshold matched to specificity of normal clinical read (56.3%) DL had sensitivity 84.8% vs 82.6% for on-site clinical read (P = 0.3). Conclusion: Deep learning improves automatic interpretation of MPI as compared to current quantitative methods.