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To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered. [ABSTRACT FROM AUTHOR]

Objectives: Previous large studies of contrast-induced or post-contrast acute kidney injury (CI-AKI/PC-AKI) have been observational, and mostly retrospective, often with patients undergoing non-enhanced CT as controls. This carries risk of inclusion bias that makes the true incidence of PC-AKI hard to interpret. Our aim was to determine the incidence of PC-AKI in a large, randomly selected cohort, comparing the serum creatinine (Scr) changes after contrast medium exposure with the normal intraindividual fluctuation in Scr.Methods: In this prospective study of 1009 participants (age 50-65 years, 48% females) in the Swedish CArdioPulmonary bioImage Study (SCAPIS), with estimated glomerular filtration rate (eGFR) ≥ 50 mL/min, all received standard dose intravenous iohexol at coronary CT angiography (CCTA). Two separate pre-CCTA Scr samples and a follow-up sample 2-4 days post-CCTA were obtained. Change in Scr was statistically analyzed and stratification was used in the search of possible risk factors.Results: Median increase of Scr post-CCTA was 0-2 μmol/L. PC-AKI was observed in 12/1009 individuals (1.2%) according to the old ESUR criteria (> 25% or > 44 μmol/L Scr increase) and 2 individuals (0.2%) when using the updated ESUR criteria (≥ 50% or ≥ 27 μmol/L Scr increase). Possible risk factors (e.g., diabetes, age, eGFR, NSAID use) did not show increased risk of developing PC-AKI. The mean effect of contrast media on Scr did not exceed the intraindividual Scr fluctuation.Conclusions: Iohexol administration to a randomly selected cohort with mildly reduced eGFR is safe, and PC-AKI is very rare, occurring in only 0.2% when applying the updated ESUR criteria.Key Points: • Iohexol administration to a randomly selected cohort, 50-65 years old with mildly reduced eGFR, is safe and PC-AKI is very rare. • Applying the updated ESUR PC-AKI criteria resulted in fewer cases, 0.2% compared to 1.2% using the old ESUR criteria in this cohort with predominantly mild reduction of renal function. • The mean effect of CM on Scr did not exceed the intraindividual background fluctuation of Scr, regardless of potential risk factors, such as diabetes or NSAID use in our cohort of 1009 individuals. [ABSTRACT FROM AUTHOR]