Reproducibility of aortic valve calcification scoring with computed tomography – An interplatform analysis

Eberhard, Matthias; Hinzpeter, Ricarda; Polacin, Malgorzata; Morsbach, Fabian; Maisano, Francesco; Nietlispach, Fabiàn; Nguyen-Kim, Thi Dan Linh; Tanner, Felix C.; Alkadhi, Hatem

doi:10.1016/j.jcct.2019.01.016

Cited by 24 publications

(11 citation statements)

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“…25,28 Quantitative evaluation of coronary artery and aortic valve calcifications on CT images with dedicated software platforms are easy to perform and are highly reproducible. 14,15 For the assessment of aortic valve calcifications, quantitative assessment has outperformed subjective assessment. 14,16 Quantitative calcification assessment not only allows for the differentiation between no, mild, moderate and severe aortic valve calcification but also allows to quantify different degrees of severe aortic valve calcification and allows to objectify sexspecific differences in valve calcifications.…”

Section: Tablementioning

confidence: 99%

“…14,15 For the assessment of aortic valve calcifications, quantitative assessment has outperformed subjective assessment. 14,16 Quantitative calcification assessment not only allows for the differentiation between no, mild, moderate and severe aortic valve calcification but also allows to quantify different degrees of severe aortic valve calcification and allows to objectify sexspecific differences in valve calcifications. 14,16,29 The presence of MAC has been related to a higher risk of cardiovascular events, cardiovascular death, and all-cause mortality.…”

Section: Tablementioning

confidence: 99%

“…14,16 Quantitative calcification assessment not only allows for the differentiation between no, mild, moderate and severe aortic valve calcification but also allows to quantify different degrees of severe aortic valve calcification and allows to objectify sexspecific differences in valve calcifications. 14,16,29 The presence of MAC has been related to a higher risk of cardiovascular events, cardiovascular death, and all-cause mortality. 5,6,9 For outcome studies, subjective semi-quantitative MAC assessment on contrast-enhanced CT with different methods were described, 5,6,17,18 which limit the comparability among these studies.…”

Section: Tablementioning

confidence: 99%

“…[11][12][13] Computed tomography (CT) enables the accurate and reproducible assessment of cardiac calcifications, including the coronary arteries and aortic valve. 3,[14][15][16] All studies evaluating the prognostic effect of MAC on outcome so far applied a subjective, semi-quantitative scoring system of MAC on contrast-enhanced CT using different methods. 5,6,17,18 For example the method described by Amat-Santos et al 5,18 used a 4-grade scoring system evaluating the circumferential involvement of the mitral annulus, whereas involvement of ≥1/2 of the circumference represents severe MAC.…”

Section: Introductionmentioning

confidence: 99%

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Mitral annular calcification in the elderly – Quantitative assessment

Eberhard

Schönenberger

Hinzpeter

et al. 2021

Journal of Cardiovascular Computed Tomography

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To determine the reliability of subjective and objective quantification of mitral annular calcification (MAC) in elderly patients with severe aortic stenosis, to define quantitative sex-and age-related reference values of MAC, and to correlate quantitative MAC with mitral valve disease. Methods: In this retrospective, IRB-approved study, we included 559 patients (268 females, median age 81 years, inter-quartile range 77-85 years) with severe aortic stenosis undergoing CT. Four independent readers performed subjective MAC categorization as follows: no, mild, moderate, and severe MAC. Two independent readers performed quantitative evaluation of MAC using the Agatston score method (Agatston MAC). Mitral valve disease was determined by echocardiography. Results: Subjective MAC categorization showed high inter-reader agreement for no (k = 0.88) and severe MAC (k = 0.75), whereas agreement for moderate (k = 0.59) and mild (k = 0.45) MAC was moderate. Intra-reader agreement for subjective MAC categorization was substantial (k = 0.69 and 0.62). Inter-and intra-reader agreement for Agatston MAC were excellent (ICC = 0.998 and 0.999, respectively), with minor inconsistencies in MAC involving the left ventricular outflow tract/aortic valve. There were significantly more women than men with MAC (n = 227, 85% versus n = 209, 72%; p < 0.001), with a significantly higher Agatston MAC (median 597, range 81-2055 versus median 244; range 0-1565; p < 0.001), particularly in patients ≥85 years of age. Agatston MAC showed an area-under-the-curve of 0.84 to diagnose mitral stenosis, whereas there was no association of Agatston MAC with mitral regurgitation (p > 0.05). Conclusions: Our study in elderly patients with severe aortic stenosis shows that quantitative MAC scoring is more reliable than subjective MAC assessment. Women show higher Agatston MAC scores than men, particularly in the elderly population. Agatston MAC shows high accuracy to diagnose mitral stenosis.

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Section: Tablementioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mitral annular calcification in the elderly – Quantitative assessment

Eberhard

Schönenberger

Hinzpeter

et al. 2021

Journal of Cardiovascular Computed Tomography

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“…AVC was defined as a region ≥ 1 mm 3 with a density of ≥ 130 Hounsfield units at the AV leaflets and annulus. Observers segmented AVC by carefully including ROIs in the AV leaflet and annulus and excluding calcium in the adjacent sinus of Valsalva, left ventricular outflow tract, or mitral annulus, and image noise or beamhardening artifact was excluded (17). From the segmented ROI, the AVC volume was measured, and an AVC score was calculated using the Agatston method (18).…”

Section: Segmentation Of Avc and Radiomics Feature Extractionmentioning

confidence: 99%

Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning

et al. 2021

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Objective We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869–0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815–0.948] and 0.899 [95% CI, 0.820–0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant ( p > 0.05 for all). Conclusion Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

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