Lien-Hsin Hu1,2, Julian Betancur1, Tali Sharir3,4, Andrew J Einstein5,6, Sabahat Bokhari5,6, Mathews B Fish7, Terrence D Ruddy8, Philipp A Kaufmann9, Albert J Sinusas10, Edward J Miller10, Timothy M Bateman11, Sharmila Dorbala12, Marcelo Di Carli12, Guido Germano1, Frederic Commandeur1, Joanna X Liang1, Yuka Otaki1, Balaji K Tamarappoo1, Damini Dey1, Daniel S Berman1, Piotr J Slomka1. 1. Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA. 2. Department of Nuclear Medicine, Taipei Veterans General Hospital, No. 201, Section 2, Shipai Rd, Taipei, Taiwan. 3. Department of Nuclear Cardiology, Assuta Medical Center, HaBarzel St 20, Tel Aviv, Israel. 4. Faculty of Health Sciences, Ben Gurion University of the Negev, Rager Blvd, 84105 Be'er Sheva, Israel. 5. Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA. 6. Department of Radiology and Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, USA. 7. Department of Nuclear Medicine, Oregon Heart and Vascular Institute, Sacred Heart Medical Center, 3333 Riverbend Dr, Springfield, OR 97477, USA. 8. Division of Cardiology, University of Ottawa Heart Institute, 40 Ruskin St, Ottawa, ON K1Y 4W7, Canada. 9. Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland. 10. Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University, 333 Cedar St, New Haven, CT 06510, USA. 11. Cardiovascular Imaging Technologies LLC, 4320 Wormhall Rd, Kansas City, 64111 MO, USA. 12. Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA.
Abstract
AIMS: To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting. METHODS AND RESULTS: A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed. CONCLUSION: In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient). Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting. METHODS AND RESULTS: A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed. CONCLUSION: In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient). Published on behalf of the European Society of Cardiology. All rights reserved.
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