Automated plaque analysis for the prognostication of major adverse cardiac events.

OBJECTIVE: The purpose of this study is to assess the value of an automated model-based plaque characterization tool for the prediction of major adverse cardiac events (MACE). METHODS: We retrospectively included 45 patients with suspected coronary artery disease of which 16 (33%) experienced MACE within 12 months. Commercially available plaque quantification software was used to automatically extract quantitative plaque morphology: lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core (LRNC) area and matrix area. The measurements were performed at all cross sections, spaced at 0.5 mm, based on fully 3D segmentations of lumen, wall, and each tissue type. Discriminatory power of these markers and traditional risk factors for predicting MACE were assessed. RESULTS: Regression analysis using clinical risk factors only resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. Based on our plaque morphology analysis, minimal cap thickness, lesion length, LRNC volume, maximal wall area/thickness, the remodeling ratio, and the calcium volume were included into our prognostic model as parameters. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a similar AUC of 0.924. CONCLUSION: An automated model based algorithm to evaluate CCTA-derived plaque features and quantify morphological features of atherosclerotic plaque increases the ability for MACE prognostication significantly compared to the use of clinical risk factors alone.

Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging.

PURPOSE: To assess the intermodel agreement of different tracer kinetic models to determine myocardial blood flow (MBF) and their diagnostic accuracy in coronary artery disease (CAD) at dynamic CT myocardial perfusion imaging (CTMPI). METHODS: Three porcine hearts perfused in Langendorff mode and 15 patients with suspected CAD and perfusion single photon emission CT (SPECT) were included. Dynamic CTMPI was performed in shuttle-mode (70 kVp, 350mAs/rot) on 3rd generation dual-source CT. In porcine hearts and patients, myocardial segments (AHA-16-segment model) were drawn. Tissue attenuation curves were constructed per segment and arterial input functions were derived from the aorta. True MBF was calculated with input flow and weight of the porcine hearts. In patients, ischemic segments were based on SPECT results. MBF quantification was performed using the VPCT-software, Upslope, Extended Toft (ET), Two-compartment (TC) and Fermi models. RESULTS: In porcine hearts, true MBF was 1.88 (interquartile range [IQR]:1.80-2.80)mL/g/min. Diagnostic accuracy was similar for all models: 0.96, 0.99, 0.92, 0.93 and 0.96 for VPCT software, Upslope method, Fermi, ET and TC model. The VPCT software and Upslope method resulted in lower MBF (median 1.44 [1.29-1.58] and 1.39 [1.25-1.59]mL/g/min) compared to the Fermi, ET, and TC model (median values of 1.76 mL/g/min [1.36-2.44], 2.55 mL/g/min [2.20-2.92], and 1.98 mL/g/min [1.60-2.60], respectively [p < 0.001]). In patients, all models showed a significant difference in MBF between the 34 ischemic and 206 non-ischemic segments (p-value<0.001). CONCLUSION: Absolute MBF values are significantly different between the models and a uniform threshold could not be determined; however, diagnostic accuracy for detecting ischemia is similar.