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Clinical Article
An integrated model based on feature fusion for classifying molecular subtypes of breast cancer
ZHANG Lei  YANG Lifeng  JIAO Xiong 

Cite this article as: ZHANG L, YANG L F, JIAO X. An integrated model based on feature fusion for classifying molecular subtypes of breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 58-64. DOI:10.12015/issn.1674-8034.2023.03.011.

[Abstract] Objective To construct an integrated support vector machine (SVM) model for classifying molecular subtypes of breast cancer by fusing traditional radiomics features and convolutional neural network features, and the value of this model for classifying molecular subtypes of breast cancer was explored.Materials and Methods One hundred and eighty-nine patients with pathologically confirmed breast cancer in the Duke-Breast-Cancer-MRI dataset were retrospectively analyzed, including 71 cases of Luminal type, 57 cases of human epidermal growth factor receptor 2 (HER-2) overexpression type, and 61 cases of triple-negative type. After preprocessing the dynamic contrast-enhanced MRI (DCE-MRI) images of all patients, the cases were divided into a training set (n=151) and testing set (n=38) in the ratio of 8∶2. The features were extracted from the region of interest (ROI) of the patient's lesion using traditional radiomics model and the DenseNet169 network model, then the traditional radiomics features were further filtered using Spearman correlation coefficient and the least absolute shrinkage and selection operator (LASSO) algorithm. Finally, the SVM-integrated classification model was constructed using the fused feature set. The macro-averaging method was used to plot the diagnostic effect of the integrated model using the receiver operating characteristic (ROC) curve.Results The traditional imaging histology features of three molecular subtypes of breast cancer were filtered to obtain 51, 49, and 20 feature labels, which were fused and modeled with 1664 features extracted by convolutional neural networks, respectively. The area under the curve (AUC) value of the classifier constructed by Luminal and HER-2 overexpression type was 0.880 [95% confidence interval (CI):0.814-0.946], the AUC value of the classifier constructed by Luminal and triple-negative type was 0.861 (95% CI: 0.791-0.931), and the AUC value of the classifier constructed by HER-2 overexpression type and triple-negative type was 0.696 (95% CI: 0.571-0.822). The AUC value of the SVM integrated model consisting of three binary classifiers was 0.820 (95% CI: 0.725-0.915).Conclusions The integrated SVM model based on feature fusion showed good results in classifying three molecular subtypes of breast cancer, which is an important guide for the preoperative classification of molecular subtypes of breast cancer.
[Keywords] breast cancer;molecular subtypes;feature fusion;support vector machine;magnetic resonance imaging

ZHANG Lei1   YANG Lifeng2   JIAO Xiong1*  

1 College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong 030600, China

2 College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China

Corresponding author: Jiao X, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Shanxi Province (No. 201801D121232).
Received  2022-10-26
Accepted  2023-03-07
DOI: 10.12015/issn.1674-8034.2023.03.011
Cite this article as: ZHANG L, YANG L F, JIAO X. An integrated model based on feature fusion for classifying molecular subtypes of breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 58-64. DOI:10.12015/issn.1674-8034.2023.03.011.

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