[en] Current guidelines and literature on screening for coronary artery calcium for cardiac risk assessment are reviewed for both general and special populations. It is shown that for both general and special populations a zero score excludes most clinically relevant coronary artery disease. The importance of standardization of coronary artery calcium measurements by multidetector CT is discussed. (orig.)

[en] Objective: The maximal diameter of an abdominal aortic aneurysm (AAA) and the change in diameter over time reflect rupture risk and are used for surgical planning. However, evidence has emerged that aneurysm volume may be a better indicator of AAA remodeling. The purpose of this study was to assess the relationship between the volume and maximal diameter of the abdominal aorta in patients with untreated infrarenal AAA. Materials and methods: This was a retrospective study of 100 patients with infrarenal AAA who were followed for more than 6 months. We examined 2 sets of computed tomography images for each patient, acquired ≥6 months apart. The maximal diameter and volume of the infrarenal abdominal aorta were determined by semiautomated segmentation software. Results: At baseline, mean maximal infrarenal diameter was 5.1 ± 1.0 cm and mean aortic volume was 139 ± 72 mL. There was good correlation between the maximal diameter and aortic volume at baseline (r² = 0.55; P < 0.001). The mean change in maximal diameter between studies was 0.2 ± 0.3 cm and the mean volume change was 19 ± 19 mL. However, the correlation between diameter change and volume change was modest (r² = 0.34; P = 0.001). Most patients (n = 64) had no measurable change in maximal diameter between studies (≤2 mm), but the change in volume was found to vary widely (−2 to 69 mL). Conclusion: In patients with untreated infrarenal AAA, a change in aortic volume can occur in the absence of a significant change in maximal diameter. Additional work is needed to examine the relationship between change in AAA volume and outcomes in this patient group.

[en] Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.