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Diagnostic performance of computed tomography-derived fractional flow reserve in patients with significant coronary
G Bastarrika1, A Álvarez-Acedo2, A Ezponda1
1Servicio de Radiología, Clínica Universidad de Navarra, Pamplona, Spain.
Insights
Computed tomography-derived fractional flow reserve (FFRCT) combined with coronary computed tomography angiography (CCTA) significantly improves diagnostic accuracy for coronary artery stenosis. This combined approach enhances the detection of hemodynamically significant blockages compared to individual methods.
Area of Science:
- Cardiovascular Imaging
- Medical Diagnostics
- Artificial Intelligence in Medicine
Background:
- Coronary artery stenosis diagnosis relies on coronary computed tomography angiography (CCTA) and invasive coronary angiography.
- Non-invasive computed tomography-derived fractional flow reserve (FFRCT) offers a potential alternative for assessing stenosis severity.
Purpose of the Study:
- To evaluate the diagnostic performance of FFRCT derived from CCTA in patients with significant coronary artery stenosis (≥50%).
- To compare the effectiveness of CCTA alone, FFRCT alone, and their combined interpretation against conventional coronary angiography.
Main Methods:
- Retrospective analysis of 73 patients with CAD-RADS 3 or 4 on CCTA.
- FFRCT calculated using deep learning software (DEEPVESSEL-FFR).
- Conventional coronary angiography used as the reference standard.
Main Results:
- A combined CCTA + FFRCT interpretation demonstrated improved diagnostic performance on both per-vessel (AUC 0.91) and per-patient (AUC 0.89) bases.
- FFRCT showed higher sensitivity (67.2%) and specificity (88.8%) for hemodynamically significant stenosis compared to CCTA alone (95.5% sensitivity, 75% specificity).
- The combined approach significantly outperformed individual CCTA and FFRCT readings (p < 0.05).
Conclusions:
- Combined interpretation of CCTA and deep learning-based FFRCT enhances diagnostic accuracy for significant coronary artery stenosis.
- This integrated approach offers a more effective non-invasive tool for evaluating coronary artery disease.
- The study highlights the potential of AI-driven FFRCT in improving cardiovascular diagnostics.
Objective:
To determine the diagnostic performance of computed tomography-derived fractional flow reserve (FFRCT) in patients with significant coronary artery stenosis (≥50%) on coronary computed tomography angiography (CCTA).
Material And Methods:
The FFRCT was retrospectively calculated for 73 patients with CAD-RADS 3 or 4 on CCTA who underwent conventional coronary angiography, using a deep learning-based software (DEEPVESSEL-FFR, Keya Medical, Kanbai Tech). A FFRCT value <0.80 was considered haemodynamically significant. Conventional coronary angiography was used as the reference standard to compare the effectiveness of the individual use of CCTA, the individual use of FFRCT and the combined use of CCTA and FFRCT.
Results:
A FFRCT of <0.80 was observed in 62 of the 219 vessels analysed (28.3%). In a per-vessel analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 95.5%, 75%, 62.7%, 97.4%, and 81.3%, respectively for ≥50% stenosis on CCTA, and 67.2%, 88.8%, 72.6%, 85.9% and 82.2%, respectively for FFRCT, when compared to conventional coronary angiography. The combined CCTA + FFRCT interpretation improved the diagnostic performance of the test both on a per-vessel (area under the curve 0.91 vs. 0.85 for CCTA; p = 0.03 and 0.78 for FFRCT; p = 0.007) and per-patient basis (area under the curve 0.89 vs. 0.81 for CCTA; p = 0.005 and 0.79 for FFRCT; p = 0.002).
Conclusion:
The combined CCTA + FFRCT interpretation with deep learning-based software improves the diagnostic performance of individual CCTA or FFRCT readings in patients with significant coronary artery stenosis.
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