Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow

Andrew Lin, Pepijn A. Van Diemen, Manish Motwani, Priscilla McElhinney, Yuka Otaki, Donghee Han, Alan Kwan, Evangelos Tzolos, Eyal Klein, Keiichiro Kuronuma, Kajetan Grodecki, Benjamin Shou, Richard Rios, Nipun Manral, Sebastien Cadet, Ibrahim Danad, Roel S. Driessen, Daniel S. Berman, Bjarne L. Nørgaard, Piotr J. SlomkaPaul Knaapen, Damini Dey

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Background: A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET). Methods: This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]‚ Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA‚ [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT). Results: One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT(0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT(area under the receiver-operating characteristic curve 0.77; P=0.16).

Original languageEnglish
Article numbere014369
Pages (from-to)710-720
Number of pages11
JournalCirculation: Cardiovascular Imaging
Volume15
Issue number10
DOIs
Publication statusPublished - 1 Oct 2022
Externally publishedYes

Keywords

  • computed tomography angiography
  • coronary atherosclerosis
  • fractional flow reserve
  • machine learning
  • positron emission tomography

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