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Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.
Zhang, Y, van der Werf, NR, Jiang, B, van Hamersvelt, R, Greuter, MJW, Xie, X
European radiology. 2020;(2):1285-1294
Abstract
OBJECTIVE To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts. METHODS Three artificial coronary arteries containing nine calcified plaques of different densities (high, medium, and low) and sizes (large, medium, and small) were attached to a moving robotic arm. The artificial arteries moving at 0-90 mm/s were scanned to generate nine categories (each from one calcified plaque) of images with motion artifacts. An inception v3 CNN was fine-tuned and validated. Agatston scores of the predicted classification by CNN were considered as corrected scores. Variation of Agatston scores on moving plaque and by CNN correction was calculated using the scores at rest as reference. RESULTS The overall accuracy of CNN classification was 79.2 ± 6.1% for nine categories. The accuracy was 88.3 ± 4.9%, 75.9 ± 6.4%, and 73.5 ± 5.0% for the high-, medium-, and low-density plaques, respectively. Compared with the Agatston score at rest, the overall median score variation was 37.8% (1st and 3rd quartile, 10.5% and 68.8%) in moving plaques. CNN correction largely decreased the variation to 3.7% (1.9%, 9.1%) (p < 0.001, Mann-Whitney U test) and improved the sensitivity (percentage of non-zero scores among all the scores) from 65 to 85% for detection of coronary calcifications. CONCLUSIONS In this experimental study, CNN showed the ability to classify motion-induced blurred images and correct calcium scores derived from nontriggered chest CT. CNN correction largely reduces the overall Agatston score variation and increases the sensitivity to detect calcifications. KEY POINTS • A deep CNN architecture trained by CT images of motion artifacts showed the ability to correct coronary calcium scores from blurred images. • A correction algorithm based on deep CNN can be used for a tenfold reduction in Agatston score variations from 38 to 3.7% of moving coronary calcified plaques and to improve the sensitivity from 65 to 85% for the detection of calcifications. • This experimental study provides a method to improve its accuracy for coronary calcium scores that is a fundamental step towards a real clinical scenario.
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Predictive value of coronary artery calcium score in cardiovascular disease.
Liu, S, Zheng, X, Xu, J, Wang, X, Zhang, Y, Lv, B, Zheng, L, Sun, K
Frontiers in bioscience (Elite edition). 2020;(1):113-125
Abstract
We investigated coronary heart disease (CHD) and cardiovascular disease (CVD) event rates in a diverse population with a coronary artery calcium score (CACS) of 0 and the role of CACS in the detection of subclinical noncalcified atherosclerotic plaque. A total of 15,884 participants in five studies were included in this meta-analysis. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated. The results showed that CHD incidence significantly increased with increased CACS (HR=0.05, 95% CI 0.03-0.06, Z=5.82, P=0.002). The CHD rate was low and further increased with CACS of 101-300. With CACS >300, the CHD rate was highest. Similarly, CVD rate was low with CACS of 0, increased with CACS of 1-100 (HR=0.03, 95% CI 0.01-0.06, Z=1.66, P=0.096), and further increased with CACS of 101-300. With CACS >300, the CVD rate was highest. Clinical evidence indicated that the higher the CACS, the higher the CHD and CVD rates, while the CVD rate does not always decreased compared with CHD rate with the same CACS, especially with CACS of 0.
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Epicardial Fat Volume Improves the Prediction of Obstructive Coronary Artery Disease Above Traditional Risk Factors and Coronary Calcium Score.
Zhou, J, Chen, Y, Zhang, Y, Wang, H, Tan, Y, Liu, Y, Huang, L, Zhang, H, Ma, Y, Cong, H
Circulation. Cardiovascular imaging. 2019;(1):e008002
Abstract
BACKGROUND Recent studies have demonstrated the tremendous potential of epicardial fat volume (EFV) to predict obstructive coronary artery disease. We aimed to develop a new model to estimate pretest probability of obstructive coronary artery disease using traditional risk factors with coronary calcium score and EFV and compare it with proposed models in Chinese patients who underwent coronary computed tomography angiography. METHODS The new models were derived from 5743 consecutive patients using multivariate logistic regression and validated in an internal cohort using invasive coronary angiography as the outcome and an external cohort with clinical outcome data. Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve, integrated discrimination improvement and net reclassification improvement were calculated to validate and compare the performance of models. RESULTS EFV improved prediction above conventional risk factors and coronary calcium score (area under the receiver operating characteristic curve increased from 0.856 to 0.874, integrated discrimination improvement 0.0487, net reclassification improvement 0.1181, P<0.0001 for all). The final model included 5 predictors: age, sex, symptom, coronary calcium score, and EFV. Good internal validation and external validation of the new model were achieved, with positive net reclassification improvement and integrated discrimination improvement, excellent area under the receiver operating characteristic curve and favorable calibration. Further, the new model demonstrated a better prediction of clinical outcome, resulting in a more cost-effective risk stratification to optimize decision-making of downstream diagnosis and treatment. CONCLUSIONS Addition of EFV to conventional risk factors and coronary calcium score offered a more accurate and effective estimation for pretest probability of obstructive coronary artery disease, which may help to improve initial management of stable chest pain.
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Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model.
Wang, M, Liu, Y, Zhou, X, Zhou, J, Zhang, H, Zhang, Y
BMC cardiovascular disorders. 2018;(1):176
Abstract
BACKGROUND Reliability of models for estimating pretest probability (PTP) of obstructive coronary artery disease (CAD) has not been investigated in individuals at low extreme of traditional risk factor (RF) burden. Thus, we sought to validate and compare CONFIRM score and Genders extended model (GEM) among these individuals. METHODS We identified symptomatic individuals with 0 or 1 RF who underwent coronary calcium scan and coronary computed tomographic angiography (CCTA). Follow-up clinical data were also recorded. PTP of obstructive CAD for every individual was estimated according to CONFIRM score and GEM, respectively. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer-Lemeshow (H-L) test were used to assess the performance of models. RESULTS There were 1201 individuals with 0 RF and 2415 with 1 RF. The AUC for GEM was significantly larger than that for CONFIRM score, no matter in individuals with 0 (0.843 v.s. 0.762, p < 0.0001) or 1 (0.823 v.s. 0.752, p < 0.0001) RF. Compared to CONFIRM score, GEM demonstrated positive IDI (5% in individuals with 0 RF and 8% in individuals with 1 RF), positive NRI (41.50% in individuals with 0 RF and 40.19% in individuals with 1 RF), better prediction of clinical events and less discrepancy between observed and predicted probabilities, resulting in a significant decrease of unnecessary testing, especially in negative individuals. CONCLUSION In individuals at low extreme of traditional RF burden of CAD, the addition of coronary calcium score provided a more accurate estimation for PTP and application of GEM instead of CONFIRM score could avoid unnecessary testing.