Prediction of Histological Grade and Ki-67 Expression inBreast Cancer by DCE-MRI and DWI Features
Zhao Wenrui1, Xu Maosheng2, Wang Shiwei2, Fan Ming1*, Li Lihua1
1.(College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China) 2.(Department of Radiology, Zhejiang Hospital of Traditional Chinese Medicine, Hangzhou 310006, China)
Abstract：The purpose of this study was to predict the histological grade of breast cancer and Ki-67 expression using features extracted from dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI). In this study, 144 cases of breast invasive ductal carcinoma were collected, which have not experienced breast surgery or chemotherapy before MRI examination. DW and DCE-MR images were obtained from preoperative breast MRI examination using a 3T scanner, and ADC map was calculated from DWI. Breast tumor segmentation was performed on all of the image series. After that, image features of texture, statistic, and morphological features of breast tumor were extracted on both the DW and DCE-MR images. The unsupervised discriminative feature selection (UDFS) and Fisher Score algorithm were used for feature selection. The classification model was established on these images respectively, and the classifiers of single-parametric image were fused for prediction. In order to evaluate the classifier performance, the area under the receiver operating characteristic curve (AUC) were calculated in a leave-one-out cross-validation analysis. The predictive model based on the second postcontrast image series of DCE-MRI generated an AUC of 0.780 with the specificity and sensitivity of 0.647 and 0.934 respectively in histological grade task, while in Ki-67 expression task, the model based on DWI generated an AUC of 0.756 with the specificity and sensitivity of 0.806 and 0.695, respectively. After multi-classifier fusion using features both from the DWI and DCE-MRI, the classification result was increased to AUC of 0.808 with the specificity and sensitivity of 0.706 and 0.895 respectively in histological grade prediction task, and generated an AUC of 0.783 with the specificity and sensitivity of 0.778 and 0.722 respectively in Ki-67 expression prediction task. In conclusion, it was showed that compared with each single parametric image alone, the performance of the classifier could be improved by combining features of DCE-MRI and DWI.
赵文芮, 许茂盛, 王世威, 范明, 厉力华. DCE-MRI及DWI影像特征对乳腺癌病理组织学分级及Ki-67表达的预测研究[J]. 中国生物医学工程学报, 2019, 38(2): 176-183.
Zhao Wenrui, Xu Maosheng, Wang Shiwei, Fan Ming, Li Lihua. Prediction of Histological Grade and Ki-67 Expression inBreast Cancer by DCE-MRI and DWI Features. Chinese Journal of Biomedical Engineering, 2019, 38(2): 176-183.
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