High-pitch dual-source CT for coronary artery calcium scoring: A head-to-head comparison of non-triggered chest versus triggered cardiac acquisition.

BACKGROUND: To determine the effect of low-dose, high-pitch non-electrocardiographic (ECG)-triggered chest CT on coronary artery calcium (CAC) detection, quantification and risk stratification, compared to ECG-triggered cardiac CT. METHODS: We selected 1,000 participants from the ImaLife study, 50% with coronary calcification on cardiac CT. All participants underwent non-contrast cardiac CT followed by chest CT using third-generation dual-source technology. Reconstruction settings were equal for both acquisitions. CAC scores were determined by Agatston's method, and divided dichotomously (0, >0), and into risk categories (0, 1-99, 100-399, ≥400). We investigated the influence of heart rate and body mass index (BMI) on risk reclassification. RESULTS: Positive CAC scores on cardiac CT ranged from 1 to 6926 (median 39). Compared to cardiac CT, chest CT had sensitivity of 0.96 (95%CI 0.94-0.98) and specificity of 0.99 (95%CI 0.97-0.99) for CAC detection (κ = 0.95). In participants with coronary calcification on cardiac CT, CAC score on chest CT was lower than on cardiac CT (median 30 versus 40, p˂0.001). Agreement in CAC-based risk strata was excellent (weighted κ = 0.95). Sixty-five cases (6.5%) were reclassified by one risk category in chest CT, with fifty-five (84.6%) shifting downward. Higher BMI resulted in higher reclassification rate (13% for BMI ≥30 versus 5.2% for BMI <30, p = 0.001), but there was no effect of heart rate. CONCLUSION: Low-dose, high-pitch chest CT, using third-generation dual-source technology shows almost perfect agreement with cardiac CT in CAC detection and risk stratification. However, low-dose chest CT mainly underestimates the CAC score as compared to cardiac CT, and results in inaccurate risk categorization in BMI ≥30.

Cardiovascular Risk Factors and Coronary Calcification in a Middle-aged Dutch Population: The ImaLife Study.

PURPOSE: To assess the presence of coronary artery calcium (CAC) and its association with cardiovascular risk factors and Systematic COronary Risk Evaluation (SCORE) risk in a middle-aged Dutch population. METHODS: Classic cardiovascular risk factors and CAC were analyzed in 4083 participants aged 45 to 60 years (57.9% women) from the population-based ImaLife study. CAC scores were quantified on noncontrast cardiac CT scans. Age-specific and sex-specific distribution of CAC categories (0, 1 to 99, 100 to 299, ≥300) and percentiles were determined. SCORE risk categories (<1%, ≥1% to 5%, and ≥5%) were compared with CAC distribution. Population attributable fractions (PAFs) of classic risk factors for CAC were estimated. RESULTS: CAC was present in 54.5% male and 26.5% female participants. The percentage of individuals with CAC increased with increasing age. Mean SCORE was 2.0% in men and 0.7% in women. In SCORE <1%, 32.7% of men and 17.1% of women had CAC. In men with SCORE ≥5%, 26.9% had no CAC. Only 0.1% of women had SCORE ≥5%. PAF of classic risk factors for CAC was 18.5% in men and 31.4% in women. PAF was highest for hypertension (in men 8.0%, 95% confidence interval, 4.2%-11.8%; in women 13.1%, 95% confidence interval, 7.9%-18.2%) followed by hypercholesterolemia and obesity. CONCLUSION: In this middle-aged cohort, more than half of the men and a quarter of the women had CAC. One out of 4 men at high risk (SCORE ≥5%) could be placed into a lower risk category owing to absence of CAC. Thus, adding CAC scoring to SCORE could have considerable effect on cardiovascular risk classification. Elimination of exposure to classic risk factors could reduce limited proportion of CAC in a middle-aged population.

Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium.

PURPOSE: Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test negative for CAC could significantly reduce the workload of radiologists. We propose an algorithm that both excludes negative scans and segments the CAC. METHOD: The training and internal validation data were collected from the ROBINSCA study. The external validation data were collected from the ImaLife study. Both contain annotated low-dose non-contrast cardiac CT scans. 60 scans of participants were used for training and 2 sets of 50 CT scans of participants without CAC and 50 CT scans of participants with an Agatston score between 10 and 20 were collected for both internal and external validation. The effect of dilated convolutional layers was tested by using 2 CNN architectures. We used the patient-level accuracy as metric for assessing the accuracy of our pipeline for detection of CAC and the Dice coefficient score as metric for the segmentation of CAC. RESULTS: Of the 50 negative cases in the internal and external validation set, 62 % and 86 % were classified correctly, respectively. There were no false negative predictions. For the segmentation task, Dice Coefficient scores of 0.63 and 0.84 were achieved for the internal and external validation datasets, respectively. CONCLUSIONS: Our algorithm excluded 86 % of all scans without CAC. Radiologists might need to spend less time on participants without CAC and could spend more time on participants that need their attention.

Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population: rationale and design of the ImaLife (Imaging in Lifelines) Study.

Lung cancer, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD) are expected to cause most deaths by 2050. State-of-the-art computed tomography (CT) allows early detection of lung cancer and simultaneous evaluation of imaging biomarkers for the early stages of COPD, based on pulmonary density and bronchial wall thickness, and of CAD, based on the coronary artery calcium score (CACS), at low radiation dose. To determine cut-off values for positive tests for elevated risk and presence of disease is one of the major tasks before considering implementation of CT screening in a general population. The ImaLife (Imaging in Lifelines) study, embedded in the Lifelines study, is designed to establish the reference values of the imaging biomarkers for the big three diseases in a well-defined general population aged 45 years and older. In total, 12,000 participants will undergo CACS and chest acquisitions with latest CT technology. The estimated percentage of individuals with lung nodules needing further workup is around 1-2%. Given the around 10% prevalence of COPD and CAD in the general population, the expected number of COPD and CAD is around 1000 each. So far, nearly 4000 participants have been included. The ImaLife study will allow differentiation between normal aging of the pulmonary and cardiovascular system and early stages of the big three diseases based on low-dose CT imaging. This information can be finally integrated into personalized precision health strategies in the general population.

Exposure to disinfection byproducts and risk of type 2 diabetes: a nested case-control study in the HUNT and Lifelines cohorts.

INTRODUCTION: Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM). OBJECTIVES: We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status. METHODS: A prospective 1:1 matched case-control study (n = 430) and a cross-sectional 1:1 matched case-control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure. RESULTS: Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 | OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 | OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97). CONCLUSION: Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.