title: Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population.
abstract: OBJECTIVE
  We aimed to improve the diagnostic accuracy of myocardial perfusion SPECT (MPS) by integrating clinical data and quantitative image features with machine learning (ML) algorithms.
  
  
  METHODS
  1,181 rest (201)Tl/stress (99m)Tc-sestamibi dual-isotope MPS studies [713 consecutive cases with correlating invasive coronary angiography (ICA) and suspected coronary artery disease (CAD) and 468 with low likelihood (LLk) of CAD <5%] were considered. Cases with stenosis <70% by ICA and LLk of CAD were considered normal. Total stress perfusion deficit (TPD) for supine/prone data, stress/rest perfusion change, and transient ischemic dilatation were derived by automated perfusion quantification software and were combined with age, sex, and post-electrocardiogram CAD probability by a boosted ensemble ML algorithm (LogitBoost). The diagnostic accuracy of the model for prediction of obstructive CAD ≥70% was compared to standard prone/supine quantification and to visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data. Tenfold stratified cross-validation was performed.
  
  
  RESULTS
  The diagnostic accuracy of ML (87.3% ± 2.1%) was similar to Expert 1 (86.0% ± 2.1%), but superior to combined supine/prone TPD (82.8% ± 2.2%) and Expert 2 (82.1% ± 2.2%) (P < .01). The receiver operator characteristic areas under curve for ML algorithm (0.94 ± 0.01) were higher than those for TPD and both visual readers (P < .001). The sensitivity of ML algorithm (78.9% ± 4.2%) was similar to TPD (75.6% ± 4.4%) and Expert 1 (76.3% ± 4.3%), but higher than that of Expert 2 (71.1% ± 4.6%), (P < .01). The specificity of ML algorithm (92.1% ± 2.2%) was similar to Expert 1 (91.4% ± 2.2%) and Expert 2 (88.3% ± 2.5%), but higher than TPD (86.8% ± 2.6%), (P < .01).
  
  
  CONCLUSIONS
  ML significantly improves diagnostic performance of MPS by computational integration of quantitative perfusion and clinical data to the level rivaling expert analysis.
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
authors.forename: Reza
authors.initials: R
authors.lastname: Arsanjani
authors.forename: Yuan
authors.initials: Y
authors.lastname: Xu
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.forename: Vishal
authors.initials: V
authors.lastname: Vahistha
authors.forename: Aryeh
authors.initials: A
authors.lastname: Shalev
authors.forename: Rine
authors.initials: R
authors.lastname: Nakanishi
authors.forename: Sean
authors.initials: S
authors.lastname: Hayes
authors.forename: Mathews
authors.initials: M
authors.lastname: Fish
authors.forename: Daniel
authors.initials: D
authors.lastname: Berman
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
mesh_terms: D000368:Aged; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D004562:Electrocardiography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000473:pathology; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D010477:Perfusion; D012372:ROC Curve; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D017256:Technetium Tc 99m Sestamibi; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*
chemical_list: D019275:Radiopharmaceuticals; D017256:Technetium Tc 99m Sestamibi