Abstract：Breast cancer is one of the malignant cancers with the highest mortality rate in women. To improve the diagnostic efficiency and provide more objective and accurate diagnosis results, we used a public data set BreaKHis of pathological images of breast tumors in 82 patients by radiomic method. We extracted grayscale features, Haralick texture features, local binary patterns (LBP) features and Gabor features of 139-dimensional radiomic features of breast tumor pathology images from the data set. The principal component analysis (PCA) was employed to reduce the dimensionality of the omics. After that we constructed a diagnostic model of breast tumors by using four different classifiers including random forest (RF), extreme learning machine (ELM), support vector machine (SVM), k-nearest neighbor (kNN) and evaluated the different feature sets mentioned above. Results showed that the classification of radiomics features based on support vector machine was the best. The accuracy rate reached 88.2%, the sensitivity reached 86.62%, and the specificity reached 89.82%. The proposed method provided a new detection solution for the prediction of benign and malignant breast tumors, which would greatly improve the accuracy of clinical diagnosis of benign and malignant breast tumors.
赵爽, 魏国辉, 马志庆, 赵文华. 基于定量影像组学的乳腺肿瘤良恶性诊断[J]. 中国生物医学工程学报, 2019, 38(5): 549-557.
Zhao Shuang, Wei Guohui, Ma Zhiqing, Zhao Wenhua. Diagnosis of Benign and Malignant Breast Tumors Using a Quantitative Radiomic Method. Chinese Journal of Biomedical Engineering, 2019, 38(5): 549-557.
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