TY - JOUR
T1 - Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy
AU - Wang, Jie
AU - Bravo, Laura
AU - Zhang, Jinquan
AU - Liu, Wen
AU - Wan, Ke
AU - Sun, Jiayu
AU - Zhu, Yanjie
AU - Han, Yuchi
AU - Gkoutos, Georgios V
AU - Chen, Yucheng
N1 - Copyright © 2021 Wang, Bravo, Zhang, Liu, Wan, Sun, Zhu, Han, Gkoutos and Chen.
PY - 2021/12/10
Y1 - 2021/12/10
N2 - Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.
Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.
Results: During a median follow-up of 29 months (interquartile range, 20-42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032-1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032-1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).
Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.
AB - Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.
Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.
Results: During a median follow-up of 29 months (interquartile range, 20-42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032-1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032-1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).
Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.
KW - hypertrophic cardiomyopathy
KW - machine learning
KW - sudden cardiac death
KW - late gadolinium enhancement
KW - radiomics
UR - https://www.scopus.com/pages/publications/85128149966
U2 - 10.3389/fcvm.2021.766287
DO - 10.3389/fcvm.2021.766287
M3 - Article
C2 - 34957254
SN - 2297-055X
VL - 8
JO - Frontiers in cardiovascular medicine
JF - Frontiers in cardiovascular medicine
M1 - 766287
ER -