Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach
- Authors
- Han, Donghee; Lee, Ji Hyun; Rizvi, Asim; Gransar, Heidi; Baskaran, Lohendran; Schulman-Marcus, Joshua; Hartaigh, Briain O.; Lin, Fay Y.; Min, James K.
- Issue Date
- Feb-2018
- Publisher
- SPRINGER
- Keywords
- Computed tomography; rest perfusion; perfusion analysis; machine learning
- Citation
- JOURNAL OF NUCLEAR CARDIOLOGY, v.25, no.1, pp.223 - 233
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF NUCLEAR CARDIOLOGY
- Volume
- 25
- Number
- 1
- Start Page
- 223
- End Page
- 233
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150540
- DOI
- 10.1007/s12350-017-0834-y
- ISSN
- 1071-3581
- 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.
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