P184Topographic pattern of valve calcification: a new determinant of disease severity in aortic valve stenosis

Santis, Antonio de; Tarasoutchi, Flávio; Filho, Jose Augusto Barreto; Katz, Marcelo; Vieira, Marcelo Carvalho; Nomura, César Higa; Spina, Guilherme Sobreira; Sampaio, Roney Orismar; Brown, Jonathan; Edelman, Elazer R.; Rosa, Vitor Emer Egypto; Fernandes, João Ricardo Cordeiro; Lemos, Pedro A.

doi:10.1093/eurheartj/ehx501.p184

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“…Because AS severity is not always proportional to the AVC burden, we expected that characteristics other than AVC burden, such as morphology, texture, and distribution, might contribute to AS severity. A previous study suggested that the tomographic pattern of AVC could be an independent determinant of AS severity because the CT attenuation ratio of the center to the periphery of the AV was higher in severe AS than in moderate AS (8). Accordingly, our study demonstrates promising results.…”

Section: Discussionsupporting

confidence: 60%

“…AS severity is influenced by other factors, including AVC attenuation, shape, symmetry, or distribution. As shown in a previous study, not only the degree of AVC but also the localization of AVC within the valve is associated with AS severity (8). The term "radiomics" refers to the highthroughput extraction of high-dimensional quantitative information from a medical image.…”

Section: Introductionmentioning

confidence: 96%

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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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Section: Discussionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 96%