Abstract
Objectives
This study investigated the discriminability of quantitative radiomics features extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and healthy (NOR) patients.
Methods
The data of two hundred and eighty-three patients with HCM (n = 48) or DCM (n = 52) and NOR (n = 123) were extracted from two publicly available datasets. Ten feature selection methods were first performed on twenty-one different sets of radiomics features extracted from the left ventricle, right ventricle, and myocardium segmented from CMR images in the end-diastolic frame, end-systolic frame, and a combination of both; then, nine classical machine learning methods were trained with the selected radiomics features to distinguish HCM, DCM, and NOR. Ninety classification models were constructed based on combinations of the ten feature selection methods and nine classifiers. The classification models were evaluated, and the optimal model was selected. The diagnostic performance of the selected model was also compared to that of state-of-the-art methods.
Results
The random forest minimum redundancy maximum relevance model with features based on LeastAxisLength, Maximum2DDiameterSlice, Median, MinorAxisLength, Sphericity, VoxelVolume, Kurtosis, Flatness, and Skewness was the highest performing model, achieving 91.2% classification accuracy. The cross-validated areas under the curve on the test dataset were 0.938, 0.966, and 0.936 for NOR, DCM, and HCM, respectively. Furthermore, compared with those of the state-of-the-art methods, the sensitivity and accuracy of this model were greatly improved.
Conclusions
A predictive model was proposed based on CMR radiomics features for classifying HCM, DCM, and NOR patients. The model had good discriminability.
Key Points
• The first-order features and the features extracted from the LOG-filtered images have potential in distinguishing HCM patients from DCM patients.
• The features extracted from the RV play little role in distinguishing DCM from HCM.
• The VoxelVolume of the myocardium in the ED frame is important in the recognition of DCM.
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Abbreviations
- AUC:
-
Area under the curve
- CMR:
-
Cardiac magnetic resonance
- DCM:
-
Dilated cardiomyopathy
- DT:
-
Decision tree
- ED:
-
End-diastole
- EL:
-
Ensemble learning
- ES:
-
End-systole
- EUDT:
-
Euclidean distance
- EUDT:
-
Euclidean distance
- FAOV:
-
F-ANOVA
- GINI:
-
Gini index
- GLCM:
-
Gray level co-occurrence matrix
- GLRLM:
-
Gray level run length matrix
- GLSZM:
-
Gray level size zone matrix
- GNRO:
-
Gain ratio
- HCM:
-
Hypertrophic cardiomyopathy
- ICC:
-
Intraclass correlation coefficient
- IFGN:
-
Information gain
- JMI:
-
Joint mutual information
- KNN:
-
K-nearest neighbor
- LOG:
-
Laplacian of Gaussian-filtered
- LR:
-
Logistic regression
- LV:
-
Left ventricle
- MIM:
-
Mutual information maximization
- MLP:
-
Multilayer perceptron
- MRMR:
-
Minimum redundancy maximum relevance
- MUIF:
-
Mutual information feature selection
- MYO:
-
Myocardium
- NB:
-
Naive Bayes
- NGDTM:
-
Neighboring gray tone difference
- ROI:
-
Region of interest
- SSFP:
-
Steady-state free procession
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Acknowledgements
The authors would like to express appreciation to American Journal Experts for providing linguistic assistance during the preparation of this paper.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62172047, 61802020).
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The scientific guarantor of this publication is Shifeng Zhao.
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Shifeng Zhao and **aoxuan Zhang have significant statistical expertise.
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• multicenter study
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Zhang, X., Cui, C., Zhao, S. et al. Cardiac magnetic resonance radiomics for disease classification. Eur Radiol 33, 2312–2323 (2023). https://doi.org/10.1007/s00330-022-09236-x
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DOI: https://doi.org/10.1007/s00330-022-09236-x