title: Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach.
abstract: Evaluation of resting myocardial computed tomography perfusion (CTP) by coronary CT angiography (CCTA) might serve as a useful addition for determining coronary artery disease. We aimed to evaluate the incremental benefit of resting CTP over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods.
  
  
  
  252 patients underwent CCTA and invasive fractional flow reserve (FFR). CT stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. Significant ischemia was defined as invasive FFR < 0.80. Resting CTP analysis was performed using a gradient boosting classifier for supervised machine learning.
  
  
  
  On a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting CTP when added to CT stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. Compared with CT stenosis [area under the receiver operating characteristic curve (AUC): 0.68, 95% confidence interval (CI) 0.62-0.74], the addition of resting CTP appeared to improve discrimination (AUC: 0.75, 95% CI 0.69-0.81, P value .001) and reclassification (net reclassification improvement: 0.52, P value < .001) of ischemia.
  
  
  
  The addition of resting CTP analysis acquired from machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis.
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.|Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Donghee
authors.initials: D
authors.lastname: Han
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.|Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Ji Hyun
authors.initials: JH
authors.lastname: Lee
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.
authors.forename: Asim
authors.initials: A
authors.lastname: Rizvi
authors.affiliation: Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Heidi
authors.initials: H
authors.lastname: Gransar
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.
authors.forename: Lohendran
authors.initials: L
authors.lastname: Baskaran
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.|Division of Cardiology, Albany Medical College, Albany, NY, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Schulman-Marcus
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.
authors.forename: Bríain
authors.initials: B
authors.lastname: Ó Hartaigh
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA.
authors.forename: Fay Y
authors.initials: FY
authors.lastname: Lin
authors.affiliation: Dalio Institute of Cardiovascular Imaging, Department of Radiology, NewYork-Presbyterian Hospital and the Weill Cornell Medicine, New York, NY, USA. runone123@gmail.com.
authors.forename: James K
authors.initials: JK
authors.lastname: Min
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D000072226:Computed Tomography Angiography; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D005260:Female; D053805:Fractional Flow Reserve, Myocardial; D006321:Heart / Q000000981:diagnostic imaging*; D006352:Heart Ventricles / Q000000981:diagnostic imaging; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia; D009206:Myocardium / Q000473:pathology*; D010477:Perfusion; D011237:Predictive Value of Tests; D011446:Prospective Studies; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed
keywords: Computed tomography; machine learning; perfusion analysis; rest perfusion