Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia

• Published in: European radiology Vol. 29; no. 5; pp. 2378 - 2387
• Main Authors: von Knebel Doeberitz, Philipp L., De Cecco, Carlo N., Schoepf, U. Joseph, Duguay, Taylor M., Albrecht, Moritz H., van Assen, Marly, Bauer, Maximilian J., Savage, Rock H., Pannell, J. Trent, De Santis, Domenico, Johnson, Addison A., Varga-Szemes, Akos, Bayer, Richard R., Schönberg, Stefan O., Nance, John W., Tesche, Christian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2019; Springer Nature B.V
• Subjects: Angiography; Arteriosclerosis; Artificial intelligence; Atherosclerosis; Blood vessels; Cardiac; Computed tomography; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary Stenosis - diagnosis; Coronary Stenosis - etiology; Coronary Stenosis - physiopathology; Diagnosis, Computer-Assisted - methods; Diagnostic Radiology; Diagnostic systems; Evaluation; Female; Fractional Flow Reserve, Myocardial - physiology; Grading; Humans; Identification methods; Imaging; Internal Medicine; Interventional Radiology; Ischemia; Learning algorithms; Lesions; Machine Learning; Male; Markers; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Multivariate analysis; Neuroradiology; Plaque, Atherosclerotic - complications; Plaque, Atherosclerotic - diagnosis; Plaque, Atherosclerotic - physiopathology; Radiology; Retrospective Studies; Risk analysis; ROC Curve; Stenosis; Ultrasound; Angiography; Coronary artery disease; Spiral computed tomography
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-018-5834-z

Objectives We 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. Methods Eighty-four patients (61 ± 10 years, 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. Results One 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  < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93). Conclusion Coronary CTA–derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power. Key Points • Coronary CT angiography (cCTA)–derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography–derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.