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| Multiparametric Magnetic Resonance Imaging Based Radiomics for Prediction of Histological Information of Breast Cancer |
| Lou Xiaofang1, Fan Ming1, Xu Maosheng2, Wang Shiwei2, Li Lihua1* |
1(Institute of Biomedical Engineering and Instrument,Hangzhou Dianzi University,Hangzhou 310018,China)
2(Department of Radiology,Zhejiang Hospital of Traditional Chinese Medicine,Hangzhou 310006,China) |
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Abstract To create a prediction model based on multiparametric magnetic resonance imaging (MRI) radiomics features extracted from dynamic enhanced magnetic resonance imaging (DCE-MRI),T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) to predict molecular subtypes,histological grade and Ki-67 expression of breast cancer. In this study,150 cases of breast invasive ductal carcinoma before surgery and chemotherapy were collected,and multiparametric images of DCE-MRI,T2WI and DWI were obtained. Breast tumor areas in the different parametric images were segmented and multiparametric imaging features were extracted. The best imaging feature subset was obtained using support vector machine recursive feature elimination (SVM-RFE) algorithm,and a prediction model based on SVM was created using the training set of each parameter imaging series. The performance of prediction model was tested in the test set. The prediction models for all parameter imaging series were fused using the probabilistic averaging method,the probabilistic voting method,and the probabilistic model optimization method. The prediction performance was evaluated by calculating the area under the ROC curve (AUC). The single-parametric imaging models discriminated among the Luminal A,Luminal B,HER2,and Basal-like subtypes with the best AUC values of 0.672 1,0.694 0,0.677 7,and 0.708 6,respectively,and the prediction performance of multiparametric imaging models was increased to AUC of 0.799 5,0.727 9,0.737 5 and 0.792 5,respectively. The single-parametric imaging models discriminated among histological grades with the best AUC values of 0.753 3,and the prediction performance of multiparametric imaging model was increased to AUC of 0.801 7. The single-parametric imaging models discriminated among Ki-67 expression with the best AUC values of 0.664 7,and the prediction performance of multiparametric imaging model was increased to AUC of 0.771 8. The prediction accuracy of multiparametric imaging models was increased significantly compared to single-parameter models (P<0.05). Our results showed that the combination of multiparametric imaging (DCE-MRI,T2WI,and DWI) radiomics could significantly improve the performance of single-parameter imaging model in predicting pathological information of breast cancer,which is of great significance for the diagnosis and selection of personalized treatment plan for breast cancer.
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Received: 16 February 2020
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