Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemiaDoeberitz, P. L. V. K., De Cecco, C. N., Schoepf, U. J., Duguay, T. M., Albrecht, M. H., van Assen, M., Bauer, M. J., Savage, R. H., Pannell, J. T., De Santis, D., Johnson, A. A., Varga-Szemes, A., Bayer, R. R., Schoenberg, S. O., Nance, J. W. & Tesche, C., May-2019, In : European Radiology. 29, 5, p. 2378-2387 10 p.
Research output: Contribution to journal › Article › Academic › peer-review
ObjectivesWe sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.MethodsEighty-four patients (6110years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.ResultsOne hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p=0.037), non-calcified plaque volume (OR 1.02, p=0.007), napkin-ring sign (OR 5.97, p=0.014), and CT-FFR (OR 0.81, p
|Number of pages||10|
|Publication status||Published - May-2019|
- Spiral computed tomography, Coronary artery disease, Angiography, COMPUTED-TOMOGRAPHY ANGIOGRAPHY, INCREMENTAL PROGNOSTIC VALUE, ASSOCIATION TASK-FORCE, AMERICAN-COLLEGE, ATHEROSCLEROTIC LESIONS, DIAGNOSTIC PERFORMANCE, ARTERY-DISEASE, PREDICTION, GUIDELINES, BURDEN