title: Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.
abstract: OBJECTIVE
  We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach.
  
  
  METHODS
  713 rest (201)Thallium/stress (99m)Technetium MPS studies with correlating invasive angiography with 372 revascularization events (275 PCI/97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation, stress combined supine/prone total perfusion deficit (TPD), supine rest and stress TPD, exercise ejection fraction, and end-systolic volume, along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). Tenfold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data.
  
  
  RESULTS
  The sensitivity of machine learning (ML) (73.6% ± 4.3%) for prediction of revascularization was similar to one reader (73.9% ± 4.6%) and standalone measures of perfusion (75.5% ± 4.5%). The specificity of ML (74.7% ± 4.2%) was also better than both expert readers (67.2% ± 4.9% and 66.0% ± 5.0%, P < .05), but was similar to ischemic TPD (68.3% ± 4.9%, P < .05). The receiver operator characteristics areas under curve for ML (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02) but superior to reader 2 (0.72 ± 0.02, P < .01) and standalone measure of perfusion (0.77 ± 0.02, P < .01).
  
  
  CONCLUSIONS
  ML approach is comparable or better than experienced readers in prediction of the early revascularization after MPS, and is significantly better than standalone measures of perfusion derived from MPS.
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA. Reza.Arsanjani@cshs.org.|David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. Reza.Arsanjani@cshs.org.
authors.forename: Reza
authors.initials: R
authors.lastname: Arsanjani
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.|David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
authors.forename: Tigran
authors.initials: T
authors.lastname: Khachatryan
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
authors.forename: Aryeh
authors.initials: A
authors.lastname: Shalev
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
authors.forename: Sean W
authors.initials: SW
authors.lastname: Hayes
authors.affiliation: Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA.
authors.forename: Mathews
authors.initials: M
authors.lastname: Fish
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
authors.forename: Rine
authors.initials: R
authors.lastname: Nakanishi
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.|David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.|David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.affiliation: Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA. piotr.slomka@cshs.org.|David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. piotr.slomka@cshs.org.|Artificial Intelligence in Medicine Program, 8700 Beverly Blvd, Ste A047N, Los Angeles, CA, 90048, USA. piotr.slomka@cshs.org.
authors.forename: Piotr
authors.initials: P
authors.lastname: Slomka
mesh_terms: D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging; D004562:Electrocardiography; D005080:Exercise Test; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging; D009204:Myocardial Revascularization; D019275:Radiopharmaceuticals / Q000737:chemistry; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D017256:Technetium Tc 99m Sestamibi / Q000737:chemistry; D013794:Thallium Radioisotopes / Q000737:chemistry; D015899:Tomography, Emission-Computed, Single-Photon
chemical_list: D019275:Radiopharmaceuticals; D013794:Thallium Radioisotopes; D017256:Technetium Tc 99m Sestamibi
keywords: Machine learning; coronary artery disease; myocardial perfusion SPECT; revascularization; total perfusion deficit