Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Donghee Han, Ji Hyun Lee, Asim Rizvi, Heidi Gransar, Lohendran Baskaran, Joshua Schulman-Marcus, Bríain ó Hartaigh, Fay Y. Lin, James K. Min

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Background: 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. 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. Results: 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. Conclusions: The addition of resting CTP analysis acquired from machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis.

Original languageEnglish
Pages (from-to)223-233
Number of pages11
JournalJournal of Nuclear Cardiology
Volume25
Issue number1
DOIs
StatePublished - 1 Feb 2018
Externally publishedYes

Keywords

  • Computed tomography
  • machine learning
  • perfusion analysis
  • rest perfusion

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