Prediction of Histological Grade in Invasive Breast Cancer Based on T2-Weighted MRI
Xie Sudan1, Fan Ming1, Xu Maosheng2, Wang Shiwei2, Li Lihua1*
1 Institute 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 invasive breast cancer based on radiomic analysis of T2-weighted magnetic resonance images (MRI). A dataset of 167 invasive breast cancer cases who had preoperative breast MRI with a 3.0 T scanner were collected. Among them, 95 cases were diagnosed as high-grade malignant (Grade 3) invasive breast cancer, while 72 were mediate-grade malignant (Grade 2). Semi-automatic lesion segmentation was performed on each T2-weighted MRI, in which 30 texture and 10 morphological features were extracted. A univariate logistic regression classifier model was implemented to evaluate the performance of the individual feature for discriminating histological grade. Various classifiers including multivariate logistic regression (MLR), support vector machines (SVM) and multi-task learning (MTL) were utilized and compared for classification. The diagnostic performance was evaluated by the area under the curve (AUC) with the receiver operating characteristic (ROC) analysis under leave-one-out cross-validation (LOOCV). P-value was calculated using Student's t test. The best single feature of morphology was the lesion radius, which was the AUC value of 0.742 and P value of 0.749×10-9. The best-performance texture feature was large zone high gray emphasis, with the AUC value of 0.742 and theP value of 0.175×10-3. The AUC values from classifiers of MLR, SVM and MTL were 0.767±0.036, 0.772±0.036 and 0.771±0.037, respectively. The values of specificity were 0.667, 0.653 and 0.708, respectively while the values of sensitivity were 0.747, 0.737 and 0.684, respectively. The results showed that T2-weighted MRI features could be utilized as promising biomarkers for predicting histological grade in invasive breast cancer.
谢素丹, 范明, 许茂盛, 王世威, 厉力华. 根据磁共振T2加权影像特征预测乳腺癌组织学分级[J]. 中国生物医学工程学报, 2020, 39(3): 280-287.
Xie Sudan, Fan Ming, Xu Maosheng, Wang Shiwei, Li Lihua. Prediction of Histological Grade in Invasive Breast Cancer Based on T2-Weighted MRI. Chinese Journal of Biomedical Engineering, 2020, 39(3): 280-287.
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