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Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Erasmus MC, Netherlands.
Univ Ulsan, South Korea.
Inst Cardiol, Poland; Inst Cardiol, Poland.
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2019 (English)In: American Journal of Roentgenology, ISSN 0361-803X, E-ISSN 1546-3141, Vol. 213, no 2, p. 325-331Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR(ML)). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFR(ML). Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFR(ML). MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFR(ML). CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFR(ML) in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFR(ML) in relation to the invasively measured FFR value was rho = 0.684, rho = 0.622, and rho = 0.669, respectively (p amp;lt; 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p amp;lt; 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p amp;lt; 0.001 for all). CONCLUSION. CCTA-derived cFFR(ML) is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

Place, publisher, year, edition, pages
AMER ROENTGEN RAY SOC , 2019. Vol. 213, no 2, p. 325-331
Keywords [en]
coronary artery disease; coronary CT angiography; fractional flow reserve; machine learning; tube voltage
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-159551DOI: 10.2214/AJR.18.20774ISI: 000477908100022PubMedID: 31039021OAI: oai:DiVA.org:liu-159551DiVA, id: diva2:1342486
Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13

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de Geer, JakobPersson, Anders
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Division of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiology in LinköpingCenter for Medical Image Science and Visualization (CMIV)
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