Magnetic resonance imaging · 2026
Non-contrast multimodal cardiac MRI for predicting coronary microvascular dysfunction in patients with hypertrophic cardiomyopathy.
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Li Jiaqi, Zhu Lingcheng, Liu Yangyingqiu, Han Yicheng, Yuan Sen, Cao Jinfeng, Li Yuxuan, Liu Xiaoqin, Shi Xinying, Luo Xin
Аннотация
OBJECTIVE: In hypertrophic cardiomyopathy (HCM), detection of coronary microcirculatory dysfunction (CMD) usually relies on contrast-enhanced cardiac magnetic resonance (CMR). This study sought to develop a practical non-contrast radiomics model to identify CMD, minimizing reliance on contrast agents.
METHODS: A total of 290 patients with HCM were stratified by the presence or absence of CMD and randomly allocated into a training set and a test set at an 8:2 ratio. The application of logistic regression was implemented to identify predictive imaging features. Radiomics features were extracted from the end-diastolic four-chamber view of the left ventricle and the end-diastolic short-axis view with maximal wall thickness across cine, T1 mapping, and T2 fat-saturation images. Five distinct machine learning algorithms were then employed to construct radiomics models, and ensemble models were generated by integrating features from different imaging planes. Model performance was evaluated in the test set using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Random forest (RF) outperformed other machine learning algorithms. Nine predictive models were constructed: S1(SAX-CINE-clinical model), F1(4CH-CINE-clinical model), and SF1 (SAX-4CH-CINE-clinical model); S2(SAX-T1 mapping-clinical model), F2(4CH-T1 mapping-clinical model), and SF2 (SAX-4CH-T1 mapping-clinical model); and S3(SAX-T2FS-clinical model), F3(4CH-T2FS-clinical model), and SF3 (SAX-4CH-T2FS-clinical model). In the test set, the SF2 model showed the best diagnostic performance, achieving an AUC of 0.90, accuracy of 0.83, sensitivity of 0.87, specificity of 0.75, and an F1 score of 0.87 for detecting coronary microcirculatory dysfunction. Calibration and decision curve analyses further demonstrated that SF2 was well-calibrated and offered superior clinical utility.
CONCLUSION: The SF2 radiomics model, integrating T1 mapping features, demonstrated the best diagnostic performance for detecting CMD in HCM patients. These findings indicate that non-contrast radiomics holds promise as a potential alternative to contrast-enhanced CMR, with the capacity to reduce reliance on contrast agents in CMD assessment.
METHODS: A total of 290 patients with HCM were stratified by the presence or absence of CMD and randomly allocated into a training set and a test set at an 8:2 ratio. The application of logistic regression was implemented to identify predictive imaging features. Radiomics features were extracted from the end-diastolic four-chamber view of the left ventricle and the end-diastolic short-axis view with maximal wall thickness across cine, T1 mapping, and T2 fat-saturation images. Five distinct machine learning algorithms were then employed to construct radiomics models, and ensemble models were generated by integrating features from different imaging planes. Model performance was evaluated in the test set using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Random forest (RF) outperformed other machine learning algorithms. Nine predictive models were constructed: S1(SAX-CINE-clinical model), F1(4CH-CINE-clinical model), and SF1 (SAX-4CH-CINE-clinical model); S2(SAX-T1 mapping-clinical model), F2(4CH-T1 mapping-clinical model), and SF2 (SAX-4CH-T1 mapping-clinical model); and S3(SAX-T2FS-clinical model), F3(4CH-T2FS-clinical model), and SF3 (SAX-4CH-T2FS-clinical model). In the test set, the SF2 model showed the best diagnostic performance, achieving an AUC of 0.90, accuracy of 0.83, sensitivity of 0.87, specificity of 0.75, and an F1 score of 0.87 for detecting coronary microcirculatory dysfunction. Calibration and decision curve analyses further demonstrated that SF2 was well-calibrated and offered superior clinical utility.
CONCLUSION: The SF2 radiomics model, integrating T1 mapping features, demonstrated the best diagnostic performance for detecting CMD in HCM patients. These findings indicate that non-contrast radiomics holds promise as a potential alternative to contrast-enhanced CMR, with the capacity to reduce reliance on contrast agents in CMD assessment.