Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography

HU Si-xian; PENG Wan-lin; ZHOU Xuan; LI Li; ZHANG Jin-ge; LIU Ke-ling; XU Xu; XIA Chun-chao; LI Zhen-lin

Volume 50 Issue 4

Jul. 2019

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JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCES) > 2019 > 50(4): 571-576.

HU Si-xian, PENG Wan-lin, ZHOU Xuan, et al. Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography[J]. Journal of Sichuan University (Medical Sciences), 2019, 50(4): 571-576.

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Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography

Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China

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Corresponding author:
LI Zhen-lin, E-mail:HX_lizhenlin@126.com
Received Date: December 30, 2018
Revised Date: May 03, 2019
Available Online: March 16, 2021
Published Date: July 19, 2019

Abstract

Abstract

Objective To determine the value of automated detection in computed tomography angiography (CTA) for cases with greater than 70% coronary stenosis.
Methods Fifty-seven patients who had both coronary CTA and digital subtraction angiography (DSA) were retrospectively recruited in this study. The patients were categorized into two groups using a cutoff value of 70% stenosis in DSA. The AW4.6 software was used to estimate the diameter and square values from the data obtained from CTA. The sensitivity (SE), specificity (SPE), positive predictive value (PPV) and negative predictive value (NPV) of the automated CTA estimations were calculated.
Results A total of 178 vessels from the 57 patients were analyzed. The automated CTA estimations had moderate to high levels of agreements (Kappa value: 0.716-0.804, P < 0.001) with the DSA diagnoses, compared with low to moderate levels of agreements (Kappa value: 0.385-0.533, P < 0.001) in manual interpretations. The square estimations generated high SE (100%) and NPV (100%) for patient diagnoses (P < 0.016 7 vs. manual interpretations). The diameter estimations generated high SPE (90.48%) and PPV (94.12%) for patient diagnoses (P < 0.016 7, vs. manual interpretations). Similarly, high SE (96.92%) and NPV (97.89%) were found for square estimations in vessel diagnoses, while high SPE (94.69%) and PPV (90.16%) were found for diameter estimations in vessel diagnoses.
Conclusion Both automated diameter and square algorithms have high accuracy for diagnosing patients with greater than 70% coronary artery stenosis. The AW4.6 can improve the detection of severe stenosis that needs stent interventions.
- Coronary artery stenosis,
- Coronary computed tomography angiography,
- Software automated detection

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Automated Estimation of Stenosis Severity in Coronary Computed Tomography Angiography

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