Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification

Figures

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  Figure 1. Schematic view of this study. (a) CMR data were collected from HCM, HHD, and HC subjects, and typical mid-ventricle cine images are shown; (b) examples of the volume of interest (VOI) for the MYO, PM, and MYO+PM together in an HC subject (visualization was performed with a 3D slicer); (c) described methods used in the feature selection; (d) shows how the data partition was performed; (e) described ML pipeline: selected MYO and PM features were evaluated for detection and differentiation performance with different ML methods; (f) results were compared between different groups and ML methods, and the evaluation was performed with the ROC curve, calibration curve, and decision curve. HCM, hypertrophic cardiomyopathy; HHD, hypertensive heart disease; VOI, volume of interest; LASSO, least absolute shrinkage and selection operator; ML, machine learning; MYO, myocardium; PM, papillary muscle; LVH, left ventricular hypertrophy.

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  Figure 2. This figure shows the inclusion and exclusion of LVH and HC subjects. All subjects were obtained from our datasets between 2018 and 2021. For the LVH group, firstly, images with inadequate quality were excluded. Secondly, subjects with myocardial infarction, systemic diseases, or cardiac surgery were further excluded. For the HC group, images with satisfactory quality were matched to the LVH group by sex and age at a 1:2 ratio.

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  Figure 3. Performance (AUC, accuracy, precision, and recall) of different models on the detection and differentiation tasks with 3 radiomics subgroups; (a–c) detection task with different ML methods using MYO, PM, and MYO+PM groups, respectively; (d–f) differentiation task with different ML methods using MYO, PM, and MYO+PM groups, respectively. SVM, support vector machine; KNN, K-nearest neighbor; RF, random forest; DT, decision tree; AB, AdaBoost.

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  Figure 4. Box plot for the best 3 MYO and 3 PM features in differentiation task. The pink and blue boxes represent the range between the first quartile to the third quartile, while the gray dots represent each single patient in the training dataset. Only features in the MYO+PM group for the differentiation task are exhibited.

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  Figure 5. ROC curves and calibration curves for differentiation task with MYO, PM, and MYO+PM groups using SVM models. Blue, green, and red lines represent the MYO, PM, and MYO+PM groups, respectively; the AUC for the ROC curves and Hosmer–Lemeshow p-values are denoted in the figure legends. The first row shows the results on the training dataset: (a) ROC curve and (b) calibration curve. The second row shows the results on the testing dataset: (c) ROC curve and (d) calibration curve.

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  Figure 6. Decision curves for clinical usefulness evaluation. (a) shows the decision curves for the training dataset with the MYO, PM, and MYO+PM groups; (b) shows the decision curves for the training dataset with the radiomics and clinical data groups; (c) shows the decision curves for the testing dataset with the MYO, PM, and MYO+PM groups; (d) shows the decision curves for the testing dataset with the radiomics and clinical data groups.

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  Figure 7. ROC curve and calibration curve for differentiation task with radiomics and clinical data using SVM model. Red, green, and blue lines represent radiomics, CMR parameters, and radiomics + CMR parameters groups, respectively. The AUC for the ROC curves and Hosmer–Lemeshow p-values are denoted in the figure legends. The first row shows the results on the training dataset: ROC curve (a) and calibration curve (b). The second row shows the results on the testing dataset: ROC curve (c) and calibration curve (d).