Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence

Jan M Brendel; Jonathan Walterspiel; Florian Hagen; Jens Kübler; Jean-François Paul; Konstantin Nikolaou; Meinrad Gawaz; Simon Greulich; Patrick Krumm; Moritz Winkelmann

doi:10.1016/j.diii.2024.01.010

Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence

Diagn Interv Imaging. 2024 Feb 16:S2211-5684(24)00035-4. doi: 10.1016/j.diii.2024.01.010. Online ahead of print.

Affiliations

¹ Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076 Germany.
² Institut Mutualiste Montsouris, Department of Radiology, Cardiac Imaging, 75014 Paris, France; Spimed-AI, 75014 Paris, France.
³ Department of Internal Medicine III, Cardiology and Angiology, University of Tübingen, 72076 Germany.
⁴ Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076 Germany. Electronic address: patrick.krumm@med.uni-tuebingen.de.

PMID: 38368176
DOI: 10.1016/j.diii.2024.01.010

Abstract

Purpose: The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.

Materials and methods: Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.

Results: A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001).

Conclusion: Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Keywords: Computed tomography; Coronary artery disease; Deep learning; Fractional flow reserve; Transcatheter aortic valve replacement (TAVI/TAVR).