Performance of prediction models for diagnosing severe aortic stenosis based on aortic valve calcium on cardiac computed tomography: Incorporation of radiomics and machine learning

Nam Gyu Kang, Young Joo Suh, Kyunghwa Han, Young Jin Kim, Byoung Wook Choi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)334-343
Number of pages10
JournalKorean journal of radiology
Issue number3
Publication statusPublished - 2021

Bibliographical note

Funding Information:
Received: February 6, 2020 Revised: May 12, 2020 Accepted: June 4, 2020 This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1C1B6007251) and a faculty research grant of Yonsei University College of Medicine (6-2018-0041). Corresponding author: Young Joo Suh, MD, PhD, Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. • E-mail: This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https:// which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher Copyright:
© 2021 The Korean Society of Radiology.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging


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