Accuracy of non-gated low-dose non-contrast chest CT with tin filtration for coronary artery calcium scoring

Minimal dose CT for left ventricular ejection fraction and combination with chest-abdomen-pelvis CT

Objectives

This prospective study tested the diagnostic accuracy, and absolute agreement with MRI of a low-dose CT protocol for left ventricular ejection fraction (LVEF) measurement. Furthermore we assessed its potential for combining it with Chest-Abdomen-Pelvis CT (CAP-CT) for a one-stop examination.

Materials & methods

Eighty-two patients underwent helical low-dose CT. Cardiac magnetic resonance imaging (MRI) was the reference standard. In fifty patients, CAP-CT was performed concurrently, using a modified injection protocol. In these, LVEF was measured with radioisotope cardiography (MUGA). Patients >18 years, without contrast media or MRI contraindications, were included. Bias was measured with Bland-Altman analysis, classification accuracy with Receiver Operating Characteristics, and inter-reader agreement with Intra-Class Correlation Coefficient (ICC). Correlation was examined using Pearson's correlation coefficients. CAP image quality was compared to previous scans with visual grading characteristics.

Results

The mean CT dose-length-product (DLP) was 51.8 mGycm, for an estimated effective dose of 1.4 mSv, compared to 5.7 mSv for MUGA. CT LVEF bias was between 2 % and 10 %, overestimating end-diastolic volume. When corrected for bias, sensitivity and specificity of 100 and 98.5 % for classifying reduced LVEF (50 % MRI value) was achieved. ICC for MUGA was significantly lower than MRI and CT. Distinction of renal medulla and cortex was reduced in the CAP scan, but proportion of diagnostic scans was not significantly different from standard protocol.

Conclusion

When corrected for inter-modality bias, CT classifies patients with reduced LVEF with high accuracy at a quarter of MUGA dose and can be combined with CAP-CT without loss of diagnostic quality.

Exploring the interplay between epicardial fat, coronary artery calcium score, and nonalcoholic fatty liver disease through non-ECG-gated chest computed tomography: A cross-sectional study

Background and Aims

This study examines the relationships between epicardial adipose tissue (EAT), nonalcoholic fatty liver disease (NAFLD), and coronary artery calcium score (CACS) using non-ECG-gated CT scans. It aims to determine the effectiveness of EAT measurements and NAFLD as predictors for coronary artery disease (CAD).

Methods

This cross-sectional study was conducted at a specialized center, focusing on individuals who underwent non-ECG-gated chest CT scans without contrast. We evaluated EAT thickness and density in three areas: the right atrioventricular groove, the free wall of the right ventricle, and the central area of the right anterior interventricular groove. Additionally, we measured CACS and determined the presence of NAFLD by comparing liver-to-spleen density ratios. Statistical analyses, including regression models, were performed using SPSS (version 26).

Results

In this study, we enrolled 365 participants, including 203 males with an average age of 47 ± 17.93 years. EAT thickness was 6.28 ± 1.62 mm, and EAT density was -96.07 ± 12.47 Hounsfield units (HU). The mean CACS was 22.27 ± 79.01, and the mean liver density was 50.01 ± 10.76 HU. A significant positive correlation was observed between EAT thickness and CACS (r = 0.208, p < 0.001). EAT density showed a significant negative correlation with CACS (r = -0.155, p = 0.003). No correlation was found between NAFLD and CACS. Univariate logistic regression analysis identified significant predictors of increased CACS, including EAT thickness (OR: 1.803), EAT density (OR: 0.671), diabetes mellitus (DM) (OR: 5.921), and hypertension (HTN) (OR: 7.414). Multivariate analysis confirmed the significance of EAT thickness (OR: 0.682), DM (OR: 3.66), and HTN (OR: 2.79) as predictors of elevated CACS.

Conclusion

Our findings demonstrate that increased EAT thickness and decreased density are associated with higher CACS. Also, both DM and HTN significantly contribute to increased CACS. These results support the inclusion of EAT measurements in cardiovascular risk assessment models to enhance diagnostic accuracy.

Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness

Background

The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm).

Methods

A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined.

Results

A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively.

Conclusions

The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

Prevalence and clinical implications of coronary artery calcium scoring on non-gated thoracic computed tomography: a systematic review and meta-analysis

OBJECTIVES

Coronary artery calcifications (CACs) indicate the presence of coronary artery disease. CAC can be found on thoracic computed tomography (CT) conducted for non-cardiac reasons. This systematic review and meta-analysis of non-gated thoracic CT aims to assess the clinical impact and prevalence of CAC.

METHODS

Online databases were searched for articles assessing prevalence, demographic characteristics, accuracy and prognosis of incidental CAC on non-gated thoracic CT. Meta-analysis was performed using a random effects model.

RESULTS

A total of 108 studies (113,406 patients) were included (38% female). Prevalence of CAC ranged from 2.7 to 100% (pooled prevalence 52%, 95% confidence interval [CI] 46-58%). Patients with CAC were older (pooled standardised mean difference 0.88, 95% CI 0.65-1.11, p < 0.001), and more likely to be male (pooled odds ratio [OR] 1.95, 95% CI 1.55-2.45, p < 0.001), with diabetes (pooled OR 2.63, 95% CI 1.95-3.54, p < 0.001), hypercholesterolaemia (pooled OR 2.28, 95% CI 1.33-3.93, p < 0.01) and hypertension (pooled OR 3.89, 95% CI 2.26-6.70, p < 0.001), but not higher body mass index or smoking. Non-gated CT assessment of CAC had excellent agreement with electrocardiogram-gated CT (pooled correlation coefficient 0.96, 95% CI 0.92-0.98, p < 0.001). In 51,582 patients, followed-up for 51.6 ± 27.4 months, patients with CAC had increased all cause mortality (pooled relative risk [RR] 2.13, 95% CI 1.57-2.90, p = 0.004) and major adverse cardiovascular events (pooled RR 2.91, 95% CI 2.26-3.93, p < 0.001). When CAC was present on CT, it was reported in between 18.6% and 93% of reports.

CONCLUSION

CAC is a common, but underreported, finding on non-gated CT with important prognostic implications.

CLINICAL RELEVANCE STATEMENT

Coronary artery calcium is an important prognostic indicator of cardiovascular disease. It can be assessed on non-gated thoracic CT and is a commonly underreported finding. This represents a significant population where there is a potential missed opportunity for lifestyle modification recommendations and preventative therapies. This study aims to highlight the importance of reporting incidental coronary artery calcium on non-gated thoracic CT.

KEY POINTS

• Coronary artery calcification is a common finding on non-gated thoracic CT and can be reliably identified compared to gated-CT. • Coronary artery calcification on thoracic CT is associated with an increased risk of all cause mortality and major adverse cardiovascsular events. • Coronary artery calcification is frequently not reported on non-gated thoracic CT.