Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach
DC Field | Value | Language |
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dc.contributor.author | Han, Donghee | - |
dc.contributor.author | Lee, Ji Hyun | - |
dc.contributor.author | Rizvi, Asim | - |
dc.contributor.author | Gransar, Heidi | - |
dc.contributor.author | Baskaran, Lohendran | - |
dc.contributor.author | Schulman-Marcus, Joshua | - |
dc.contributor.author | Hartaigh, Briain O. | - |
dc.contributor.author | Lin, Fay Y. | - |
dc.contributor.author | Min, James K. | - |
dc.date.accessioned | 2022-07-12T12:53:57Z | - |
dc.date.available | 2022-07-12T12:53:57Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 1071-3581 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150540 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Ji Hyun | - |
dc.identifier.doi | 10.1007/s12350-017-0834-y | - |
dc.identifier.scopusid | 2-s2.0-85015712071 | - |
dc.identifier.wosid | 000423585200036 | - |
dc.identifier.bibliographicCitation | JOURNAL OF NUCLEAR CARDIOLOGY, v.25, no.1, pp.223 - 233 | - |
dc.relation.isPartOf | JOURNAL OF NUCLEAR CARDIOLOGY | - |
dc.citation.title | JOURNAL OF NUCLEAR CARDIOLOGY | - |
dc.citation.volume | 25 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 223 | - |
dc.citation.endPage | 233 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Cardiovascular System & Cardiology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Cardiac & Cardiovascular Systems | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | FRACTIONAL FLOW RESERVE | - |
dc.subject.keywordPlus | AMERICAN-HEART-ASSOCIATION | - |
dc.subject.keywordPlus | DIAGNOSTIC PERFORMANCE | - |
dc.subject.keywordPlus | CT ANGIOGRAPHY | - |
dc.subject.keywordPlus | STRESS | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | ATHEROSCLEROSIS | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | GUIDELINES | - |
dc.subject.keywordPlus | CARDIOLOGY | - |
dc.subject.keywordAuthor | Computed tomography | - |
dc.subject.keywordAuthor | rest perfusion | - |
dc.subject.keywordAuthor | perfusion analysis | - |
dc.subject.keywordAuthor | machine learning | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s12350-017-0834-y | - |
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