Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk
Hongbo Zhang1, Lei Zhao1, Haoru Wang1
1From the Department of Interventional Diagnosis and Treatment (H.Z., K.H., C.Z., X.M.) and Department of Radiology (H.Z., L.Z., Y.Y., K.H.), Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing 100020, China; and Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China (H.W.).
Insights
A new model combining radiomics from cardiac MRI with clinical data accurately predicts heart failure (HF) risk in hypertrophic cardiomyopathy (HCM) patients. This tool aids in identifying high-risk individuals for timely intervention.
Area of Science:
- Cardiovascular Imaging and Radiology
- Biomedical Data Science
- Cardiology and Cardiovascular Diseases
Background:
- Hypertrophic cardiomyopathy (HCM) is a primary cause of heart failure (HF) in affected patients.
- Accurate risk stratification for HF in HCM is crucial for timely clinical management and improved outcomes.
- Traditional risk assessment models may not fully capture the complexity of HCM progression to HF.
Purpose of the Study:
- To develop and validate a predictive model integrating radiomics features from cardiac MRI cine images.
- To combine radiomics with clinical and standard cardiac MRI parameters for enhanced identification of high-risk HCM patients.
- To assess the model's performance in predicting heart failure events in patients with hypertrophic cardiomyopathy.
Main Methods:
- Retrospective analysis of 516 hypertrophic cardiomyopathy patients undergoing cardiac MRI.
- Extraction of radiomics features from cardiac cine images and calculation of radiomics scores using LASSO Cox regression.
- Development of a combined prediction model incorporating significant radiomics scores, clinical, and standard cardiac MRI predictors.
Main Results:
- The radiomics score emerged as the strongest independent predictor of heart failure events (HR, 10.25; P < .001).
- The combined model demonstrated high predictive accuracy, with 1- and 3-year AUCs of 0.81/0.80 (training) and 0.82/0.77 (validation).
- High-risk stratification using the model indicated a >6-fold increased risk of heart failure events.
Conclusions:
- A combined model integrating radiomics features with clinical and standard cardiac MRI parameters accurately identifies patients with hypertrophic cardiomyopathy at high risk for heart failure.
- This approach offers a promising tool for precise risk stratification in hypertrophic cardiomyopathy, potentially guiding therapeutic strategies.
- Radiomics analysis of cardiac MRI provides novel insights into disease progression and HF risk in hypertrophic cardiomyopathy.
Abstract:
Purpose To develop a model integrating radiomics features from cardiac MR cine images with clinical and standard cardiac MRI predictors to identify patients with hypertrophic cardiomyopathy (HCM) at high risk for heart failure (HF). Materials and Methods In this retrospective study, 516 patients with HCM (median age, 51 years [IQR: 40-62]; 367 [71.1%] men) who underwent cardiac MRI from January 2015 to June 2021 were divided into training and validation sets (7:3 ratio). Radiomics features were extracted from cardiac cine images, and radiomics scores were calculated based on reproducible features using the least absolute shrinkage and selection operator Cox regression. Radiomics scores and clinical and standard cardiac MRI predictors that were significantly associated with HF events in univariable Cox regression analysis were incorporated into a multivariable analysis to construct a combined prediction model. Model performance was validated using time-dependent area under the receiver operating characteristic curve (AUC), and the optimal cutoff value of the combined model was determined for patient risk stratification. Results The radiomics score was the strongest predictor for HF events in both univariable (hazard ratio, 10.37; P < .001) and multivariable (hazard ratio, 10.25; P < .001) analyses. The combined model yielded the highest 1- and 3-year AUCs of 0.81 and 0.80, respectively, in the training set and 0.82 and 0.77 in the validation set. Patients stratified as high risk had more than sixfold increased risk of HF events compared with patients at low risk. Conclusion The combined model with radiomics features and clinical and standard cardiac MRI parameters accurately identified patients with HCM at high risk for HF. Keywords: Cardiomyopathies, Outcomes Analysis, Cardiovascular MRI, Hypertrophic Cardiomyopathy, Radiomics, Heart Failure Supplemental material is available for this article. © RSNA, 2024.
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