Key result
A radiomics prediction model using LASSO and XGBoost achieved a c-index of 0.921 for diagnosing severe aortic stenosis, which was not significantly higher than models based on AVC volume or score alone.
Why the study?
The study aimed to develop a prediction model for diagnosing severe aortic stenosis using CT radiomics features of aortic valve calcium and machine learning algorithms.
Does a prediction model using CT radiomics features of aortic valve calcium and machine learning improve the diagnosis of severe aortic stenosis compared to AVC volume and score?
Observational (n=408)
Single-blind
No
Does a prediction model using CT radiomics features of aortic valve calcium and machine learning improve the diagnosis of severe aortic stenosis compared to AVC volume and score?
Absolute Event Rate: 0.921% vs 0.894%
p-value: p=>0.05
Machine learning models incorporating CT radiomics features of aortic valve calcium show high diagnostic performance for severe aortic stenosis, though not statistically superior to AVC volume or score alone.
May aid CT-based severe AS detection via radiomics ML; leaves open prospective validation before clinical use.
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: > 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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Kang et al. (2020) conducted an observational in Severe aortic stenosis (n=408). Radiomics prediction model (LASSO + XGBoost) of aortic valve calcium vs. AVC volume and AVC score models was evaluated on Discrimination ability (c-index) for diagnosing severe aortic stenosis (95% CI 0.869-0.973, p=>0.05). A radiomics prediction model using LASSO and XGBoost achieved a c-index of 0.921 for diagnosing severe aortic stenosis, which was not significantly higher than models based on AVC volume or score alone.
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