잠시만 기다려 주세요. 로딩중입니다.

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

Korean Journal of Radiology 2021년 22권 3호 p.334 ~ 343
강남규, 서영주, 한경화, 김영진, 최병욱,
소속 상세정보
강남규 ( Kang Nam-Gyu ) - Yonsei University College of Medicine Severance Hospital Department of Radiology
서영주 ( Suh Young-Joo ) - Yonsei University College of Medicine Severance Hospital Department of Radiology
한경화 ( Han Kyung-Hwa ) - Yonsei University College of Medicine Severance Hospital Department of Radiology
김영진 ( Kim Young-Jin ) - Yonsei University College of Medicine Severance Hospital Department of Radiology
최병욱 ( Choi Byoung-Wook ) - Yonsei University College of Medicine Severance Hospital Department of Radiology

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.

키워드

Aortic valve calcium; Aortic stenosis; Computed tomography; Radiomics; Machine learning

원문 및 링크아웃 정보

  

등재저널 정보