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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

Authors
 Nam Gyu Kang  ;  Young Joo Suh  ;  Kyunghwa Han  ;  Young Jin Kim  ;  Byoung Wook Choi 
Citation
 KOREAN JOURNAL OF RADIOLOGY, Vol.22(3) : 334-343, 2021-03 
Journal Title
 KOREAN JOURNAL OF RADIOLOGY 
ISSN
 1229-6929 
Issue Date
2021-03
MeSH
Aged ; Aortic Valve / diagnostic imaging* ; Aortic Valve / metabolism ; Aortic Valve Stenosis / diagnosis* ; Aortic Valve Stenosis / diagnostic imaging ; Aortic Valve Stenosis / pathology ; Calcium / metabolism* ; Echocardiography ; Female ; Humans ; Machine Learning* ; Male ; Middle Aged ; Retrospective Studies ; Severity of Illness Index ; Tomography, X-Ray Computed
Keywords
Aortic stenosis ; Aortic valve calcium ; Computed tomography ; Machine learning ; Radiomics
Abstract
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.
Files in This Item:
T202100564.pdf Download
DOI
10.3348/kjr.2020.0099
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Young Jin(김영진) ORCID logo https://orcid.org/0000-0002-6235-6550
Suh, Young Joo(서영주) ORCID logo https://orcid.org/0000-0002-2078-5832
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182186
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