Histogram-derived modified thresholds for coronary artery calcium scoring with lower tube voltage.
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ABSTRACT:
SUBMITTER: Kim S
PROVIDER: S-EPMC8408203 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To determine if coronary artery calcium (CAC) scoring using computed tomography at 80 kilovolt-peak (kVp) and 70-kVp and tube voltage-adapted scoring-thresholds allow for accurate risk stratification as compared to the standard 120-kVp protocol. We prospectively included 170 patients who underwent standard CAC scanning at 120-kVp and 200 milliamperes and additional scans with 80-kVp and 70-kVp tube voltage with adapted tube current to normalize image noise across scans. Novel kVp-adapted thresholds were applied to calculate CAC scores from the low-kVp scans and were compared to those from standard 120-kVp scans by assessing risk reclassification rates and agreement using Kendall's rank correlation coefficients (Τb) for risk categories bounded by 0, 1, 100, and 400. Interreader reclassification rates for the 120-kVp scans were assessed. Agreement for risk classification obtained from 80-kVp and 70-kVp scans as compared to 120-kVp was good (Τb = 0.967 and 0.915, respectively; both p < 0.001) with reclassification rates of 7.1% and 17.2%, respectively, mostly towards a lower risk category. By comparison, the interreader reclassification rate was 4.1% (Τb = 0.980, p < 0.001). Reclassification rates were dependent on body mass index (BMI) with 7.1% and 13.6% reclassifications for the 80-kVp and 70-kVp scans, respectively, in patients with a BMI < 30 kg/m2 (n = 140), and 2.9% and 7.4%, respectively, in patients with a BMI < 25 kg/m2 (n = 68). Mean effective radiation dose from the 120-kVp, the 80-kVp, and 70-kVp scans was 0.54 ± 0.03, 0.42 ± 0.02, and 0.26 ± 0.02 millisieverts. CAC scoring with reduced tube voltage allows for accurate risk stratification if kVp-adapted thresholds for calculation of CAC scores are applied.ClinicalTrials.gov NCT03637231.
| S-EPMC10509109 | biostudies-literature
Project description:BackgroundIn patients with suspected obstructive coronary artery disease (CAD), the risk factor-weighted clinical likelihood (RF-CL) model and the coronary artery calcium score-weighted clinical likelihood (CACS-CL) model improves the identification of obstructive CAD compared with basic pretest probability (PTP) models.ObjectivesThe aim of this study was to assess the prognostic value of the new models.MethodsThe incidences of myocardial infarction and death were stratified according to categories by the RF-CL and CACS-CL and compared with categories by the PTP model. We used cohorts from a Danish register (n = 41,177) and a North American randomized study (n = 3,952). All patients were symptomatic and were referred for diagnostic testing because of clinical indications.ResultsDespite substantial down-reclassification of patients to a likelihood ≤5% of CAD with either the RF-CL (45%) or CACS-CL (60%) models compared with the PTP (18%), the annualized event rates of myocardial infarction and death were low using all 3 models; RF-CL 0.51% (95% CI: 0.46-0.56), CACS-CL 0.48% (95% CI: 0.44-0.56), and PTP 0.37% (95% CI: 0.31-0.44), respectively. Overall, comparison of the predictive power of the 3 models using Harrell's C-statistics demonstrated superiority of the RF-CL (0.64 [95% CI: 0.63-0.65]) and CACS-CL (0.69 [95% CI: 0.67-0.70]) compared with the PTP model (0.61 [95% CI: 0.60-0.62]).ConclusionsThe simple clinical likelihood models that include classical risk factors or risk factors combined with CACS provide improved risk stratification for myocardial infarction and death compared with the standard PTP model. Hence, the optimized RF-CL and CACS-CL models identify 2.5 and 3.3 times more patients, respectively, who may not benefit from further diagnostic testing.
| S-EPMC10466200 | biostudies-literature
Project description:Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm3/scan. The proposed method improved the sensitivity to 0.80 and reduced average false positive volume of 20 mm3/scan. The results indicate that the unsupervised domain adaptation approach enables automatic CAC scoring in contrast enhanced CT while learning from a large and diverse set of CT scans without contrast. This may allow for better utilization of existing annotated data sets and extend the applicability of automatic CAC scoring to contrast-enhanced CT scans without the need for additional manual annotations. The code is publicly available at https://github.com/qurAI-amsterdam/CACscoringUsingDomainAdaptation.
| S-EPMC9510682 | biostudies-literature
Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.
| S-EPMC9755106 | biostudies-literature
Project description:ObjectiveTo evaluate interest in coronary artery calcium (CAC) among the general public during the past 17 years and to compare trends with real-world data on number of CAC procedures performed.MethodsWe used Google Trends, a publicly available database, to access search query data in a systematic and quantitative fashion to search for CAC-related key terms. Search terms included calcium test, heart score, calcium score, coronary calcium, and calcium test score. We accessed Google Trends in January 2021 and analyzed data from 2004 to 2020.ResultsFrom 2004 to December 31, 2020, CAC-related search interest (in relative search volume) increased continually worldwide (+201.9%) and in the United States (+354.8%). Three main events strongly influenced search interest in CAC: reports of a CAC scan of the president of the United States led to a transient 10-fold increase in early January 2018. American College of Cardiology/American Heart Association guideline release led to a sustained increase, and lockdown after the global pandemic due to COVID-19 led to a transient decrease. Real-world data on performed CAC scans showed an increase between 2006 and 2017 (+200.0%); during the same time period, relative search volume for CAC-related search terms increased in a similar pattern (+70.6%-1511.1%). For the search term coronary calcium scan near me, a potential representative of active online search for CAC scanning, we found a +28.8% increase in 2020 compared with 2017.ConclusionGoogle Trends, a valuable tool for assessing public interest in health-related topics, suggests increased overall interest in CAC during the last 17 years that mirrors real-world usage data. Increased interest is seemingly linked to reports of CAC testing in world leaders and endorsement in major guidelines.
| S-EPMC8105517 | biostudies-literature
Project description:ObjectivesTo determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence.MethodsThis retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19-79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups.ResultsThe overall mean ± standard deviation (SD) PCATMA was - 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: - 88.5 ± 10.5 HU versus - 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was - 92.4 ± 11.6 HU, - 88.4 ± 9.9 HU, and - 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was - 95.6 ± 9.6 HU, - 90.2 ± 11.5 HU, - 87.3 ± 9.9 HU, - 82.7 ± 6.2 HU, and - 79.3 ± 6.8 HU, respectively (p < 0.001).ConclusionsIn patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting.Key points• In patients without plaque on cCTA, PCATMA differs slightly by coronary artery (LAD, LCX, RCA). • Tube voltage of cCTA affects PCATMA measurement, with mean PCATMA increasing linearly with increasing kV. • For longitudinal cCTA analysis of PCATMA , the use of equal kV setting is strongly recommended.
| S-EPMC7599179 | biostudies-literature
Project description:Current guidelines recommend that coronary artery calcium (CAC) screening should only be used for intermediate risk groups (Framingham risk score [FRS] of 10%-20%). The CAC distributions and coronary artery disease (CAD) prevalence in various FRS strata were determined. The benefit to lower risk populations of CAC score-based screening was also assessed. In total, 1,854 participants (aged 40-79 years) without history of CAD, stroke, or diabetes were enrolled. CAC scores of > 0, ≥ 100, and ≥ 300 were present in 33.8%, 8.2%, and 2.9% of the participants, respectively. The CAC scores rose significantly as the FRS grew more severe (P < 0.01). The total CAD prevalence was 6.1%. The occult CAD prevalence in the FRS ≤ 5%, 6%-10%, 11%-20%, and > 20% strata were 3.4%, 6.7%, 9.0%, and 11.6% (P < 0.001). In multivariate logistic regression analysis adjusting, not only the intermediate and high risk groups but also the low risk (FRS 6%-10%) group had significantly increased odds ratio for occult CAD compared to the very low-risk (FRS ≤ 5%) group (1.89 [95% confidence interval, CI, 1.09-3.29] in FRS 6%-10%; 2.48 [95% CI, 1.47-4.20] in FRS 11%-20%; and 3.10 [95% CI, 1.75-5.47] in FRS > 20%; P < 0.05). In conclusion, the yield of screening for significant CAC and occult CAD is low in the very low risk population but it rises in low and intermediate risk populations.
| S-EPMC4853669 | biostudies-literature
Project description:AimsSymptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA).Methods and resultsThe study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features.ConclusionA ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.
| S-EPMC7849944 | biostudies-literature
Project description:Background and aimsWe sought to understand the risk factor correlates of very early coronary artery calcium (CAC), and the potential investigational value of CAC phenotyping in adults aged 20-30 years.MethodsWe studied all participants aged 20-30 years at baseline (N = 373) in the Coronary Artery Calcium Consortium, a large multi-center cohort study of patients aged 18 years or older without known atherosclerotic cardiovascular disease (ASCVD) at baseline, referred for CAC scoring for clinical risk stratification. We described the prevalence of CAC in men and women, the frequency of risk factors by the presence of CAC (CAC = 0 vs CAC >0), and assessed the association between traditional non-demographic CVD risk factors (hypertension, hyperlipidemia, smoking, family history of CHD, and diabetes) and prevalent CAC, using age- and sex-adjusted logistic regression models.ResultsThe mean age of the study participants was 27.5 ± 2.4 years; 324 (86.9%) had CAC = 0, and 49 (13.1%) had CAC >0. Among the 49 participants with CAC, 38 (77.6%) were men, and median CAC score was low at 4.6. In age- and sex-adjusted models, there was a graded increase in the odds of CAC >0 with increasing traditional cardiovascular disease (CVD) risk factor burden (p = 0.001 for linear trend). Participants with ≥3 traditional risk factors had a statistically significant higher odds of having prevalent CAC (OR 5.57, 95% CI; 1.82-17.03) compared to participants with no risk factors.ConclusionsOur study demonstrates the non-negligible prevalence of CAC among very high-risk young US adults, reinforcing the critical importance of traditional risk factors in the earliest development of detectable subclinical ASCVD.
| S-EPMC7260100 | biostudies-literature
Project description:ObjectivesThe objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT.MethodsA total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared.ResultsOn a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery.ConclusionsFully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings.
| S-EPMC9406755 | biostudies-literature