Details

Autor(en) / Beteiligte

• Betancur, Julian
• Hu, Lien-Hsin
• Commandeur, Frederic
• Sharir, Tali
• Einstein, Andrew J
• Fish, Mathews B
• Ruddy, Terrence D
• Kaufmann, Philipp A
• Sinusas, Albert J
• Miller, Edward J
• Bateman, Timothy M
• Dorbala, Sharmila
• Di Carli, Marcelo
• Germano, Guido
• Otaki, Yuka
• Liang, Joanna X
• Tamarappoo, Balaji K
• Dey, Damini
• Berman, Daniel S
• Slomka, Piotr J

Titel

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

Ist Teil von

• Journal of Nuclear Medicine, 2019-05, Vol.60 (5), p.664-670

Ort / Verlag

United States: Society of Nuclear Medicine

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress Tc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 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 sex- 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, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. 718 (62%) patients and 1,272 of 3,480 (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; < 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) ( < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) ( < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read ( = 0.3). DL improves automatic interpretation of MPI as compared with current quantitative methods.