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BMC Med Imaging ; 17(1): 61, 2017 12 13.
Article in English | MEDLINE | ID: mdl-29237404

ABSTRACT

BACKGROUND: Primary to validate a commercial semi-automated computed tomography angiography (CTA) -software for vulnerable plaque detection compared to histology of carotid endarterectomy (CEA) specimens and secondary validating calcifications scores by in vivo CTA with ex vivo non-contrast enhanced computed tomography (NCCT). METHODS: From January 2014 to October 2016 53 patients were included retrospectively, using a cross-sectional design. All patients underwent both CTA and CEA. Sixteen patients had their CEA specimen NCCT scanned. The semi-automated CTA software analyzed carotid stenosis using different HU values defining plaque components. The predictive values of CTA based detection of vulnerable plaques were calculated. Quantification of calcifications on CTA using region of interest (ROI)-function and mathematical equations was done manually, and validated by NCCT of the CEA specimen. RESULTS: The semi-automated CTA software had a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 89.1% (95% CI, 73.6% - 96.4%), 31.3% (95% CI, 12.1% - 58.5%), 75% (95% CI, 59.3% - 86.2%) and 55.6% (95% CI, 22.6% - 84.6%). Strong correlation between in vivo CTA and ex vivo NCCT in quantification of calcification was observed, but CTA systematically underestimated calcificationsscore (CALS) with increasing calcification. CONCLUSION: The CTA-software cannot be used in risk assessment of patients, due to poor specificity and NPV. The correlation between in vivo CTA and ex vivo NCCT was strong, proposing it to be used in both scientifically and clinical settings, but studies with larger sample sizes are needed.


Subject(s)
Carotid Stenosis/diagnostic imaging , Carotid Stenosis/pathology , Endarterectomy, Carotid/methods , Tomography, X-Ray Computed/methods , Aged , Aged, 80 and over , Carotid Stenosis/surgery , Computed Tomography Angiography/methods , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Software
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