Impact of Several Factors on the Diagnostic Interpretability of Coronary Computed Tomographic Angiography Using a 256-Detector Row CT Scanner

Abstract

Purpose: To explore the impact of patient-related, vessel-related, image quality-related and cardiovascular risk factors on CCTA interpretability using 256-detector row Computed Tomography (CT). Methods: One hundred ten patients who underwent CCTA and Invasive Coronary Angiography (ICA) were consecutively retrospectively enrolled from January 2018 to October 2018. Using ICA as the reference standard, ≥50% diameter stenosis was defined as the cut-off criterion to detect the diagnostic performance of CCTA. Diagnostic reproducibility was investigated by calculating the interrater reproducibility of CCTA. Multiple logistic regression models were performed to evaluate the impact of 14 objective factors. Results: A total of 1019 segments were evaluated. The per-segment sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CCTA were 76.8%, 93.7%, 91.2%, 67.8% and 95.9%, respectively. The per-segment diagnostic reproducibility was 0.44 for CCTA. Regarding accuracy, a negative association was found for stenosis severity, calcium load and hyperlipidaemia. Regarding sensitivity, calcium load and diabetes mellitus (DM) were positively related. Regarding specificity, a negative correlation was observed between stenosis severity and calcium load. Regarding interrater reproducibility, stenosis severity and calcium load were negatively associated, whereas male sex and the signal-to-noise ratio (SNR) were positively related (all p<0.05). Conclusion: Per-segment 256-detector row CCTA performance was optimal in stenosis-free or occluded segments. Heavier calcium load was associated with poorer CCTA interpretability. On the one hand, our findings confirmed the rule-out value of CCTA; on the other hand, they suggested improvements in calcium subtractions and deep learning-based tools to improve CCTA diagnostic interpretability.