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Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT
Yuehua Li1, Mengmeng Yu1, Xu Dai1
1From the Institute of Diagnostic and Interventional Radiology (Y.L., M.Y., X.D., J.Z.) and Department of Cardiology (Z.L., C.S.), Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China 200233; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China (Y.W.); and Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (B.L.).
Insights
Dynamic CT myocardial perfusion imaging accurately assesses coronary artery stenosis significance. Myocardial blood flow derived from this technique outperformed machine learning-based CT fractional flow reserve in identifying ischemic lesions.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Medical Diagnostics
Background:
- Direct comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) for diagnosing coronary artery disease is lacking.
- Assessing the functional significance of coronary stenosis is crucial for patient management.
Purpose of the Study:
- To evaluate the diagnostic performance of dynamic CT MPI and ML-based CT FFR in assessing coronary stenosis.
- To compare these non-invasive methods against invasive conventional coronary angiography (CCA) FFR.
Main Methods:
- Prospective enrollment of stable angina patients undergoing dynamic CT MPI, coronary CT angiography, and invasive FFR.
- Receiver operating characteristic (ROC) curve analysis to determine diagnostic accuracy.
- Analysis of myocardial blood flow (MBF) and CT FFR values in relation to stenosis severity.
Main Results:
- Dynamic CT MPI demonstrated lower myocardial blood flow (MBF) in ischemic segments (75 mL/100 mL/min) compared to non-ischemic segments (148 mL/100 mL/min).
- ML-based CT FFR was lower for significant lesions (0.68) versus nonsignificant lesions (0.83).
- MBF showed superior diagnostic performance (AUC=0.97) compared to ML-based CT FFR (AUC=0.85), with higher specificity (93% vs 68%) and accuracy (94% vs 78%).
Conclusions:
- Dynamic CT MPI accurately evaluates hemodynamic significance of coronary stenosis with reduced radiation exposure.
- Myocardial blood flow derived from dynamic CT MPI is a more effective tool than ML-based CT FFR for detecting ischemia-causing coronary lesions.
Abstract:
Background Direct intraindividual comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) has not been explored for diagnosing hemodynamically significant coronary artery disease. Purpose To investigate the diagnostic performance of dynamic CT MPI and ML-based CT FFR for functional assessment of coronary stenosis. Materials and Methods Between January 2, 2017, and October 17, 2018, consecutive participants with stable angina were prospectively enrolled. All participants underwent dynamic CT MPI coronary CT angiography and invasive conventional coronary angiography (CCA) FFR within 2 weeks. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance. Results Eighty-six participants (mean age, 67 years ± 12 [standard deviation]; 67 men) with 157 target vessels were included for final analysis. The mean radiation doses for dynamic CT MPI and coronary CT angiography were 3.6 mSv ± 1.1 and 2.7 mSv ± 0.8, respectively. Myocardial blood flow (MBF) was lower in ischemic segments compared with nonischemic segments and reference segments (defined as the territory of vessels without stenosis) (75 mL/100 mL/min ± 20 vs 148 mL/100 mL/min ± 22 and 169 mL/100 mL/min ± 34, respectively, both P < .001). Similarly, CT FFR was also lower for hemodynamically significant lesions than for hemodynamically nonsignificant lesions (0.68 ± 0.1 vs 0.83 ± 0.1, respectively, P < .001). MBF had the largest area under the ROC curve (AUC) (using 99 mL/100 mL/min as a cutoff) among all parameters, outperforming ML-based CT FFR (AUC = 0.97 vs 0.85, P < .001). The vessel-based specificity and diagnostic accuracy of MBF were higher than those of ML-based CT FFR (93% vs 68%, P < .001 and 94% vs 78%, respectively, P = .04) whereas the sensitivity of both methods was similar (96% vs 88%, respectively, P = .11). Conclusion Dynamic CT myocardial perfusion imaging was able to help accurately evaluate the hemodynamic significance of coronary stenosis using a reduced amount of radiation. In addition, the myocardial blood flow derived from dynamic CT myocardial perfusion imaging outperformed machine learning-based CT fractional flow reserve for identifying lesions causing ischemia. © RSNA, 2019 Online supplemental material is available for this article.See also the editorial by Loewe in this issue.
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