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Cardiac magnetic resonance radiomics for disease classification

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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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Notes

  1. https://acdc.creatis.insalyon.fr/description/databases.html

  2. https://www.ub.edu/mnms/

  3. https://pyradiomics.readthedocs.io/en/latest/features.html

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).

Author information

Author notes

  1. Xiaoxuan Zhang and Caixia Cui contributed equally to this work.

Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Xiaoxuan Zhang, Shifeng Zhao & Yun Tian

  2. Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China

    Caixia Cui

  3. MR Research China, GE Healthcare, Beijing, 100176, China

    Lizhi Xie

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Correspondence to Shifeng Zhao or Yun Tian.

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The scientific guarantor of this publication is Shifeng Zhao.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Shifeng Zhao and Xiaoxuan Zhang have significant statistical expertise.

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Written informed consent was obtained from all subjects (patients) in this 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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