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.
Zhao Shuang,Wei Guohui,Ma Zhiqing, et al. Diagnosis of Benign and Malignant Breast Tumors Using a Quantitative Radiomic Method[J]. Chinese Journal of Biomedical Engineering, 2019, 38(5): 549-557.
[1] Mcguire S. World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press,2015[J]. Advances in Nutrition,2016, 7(2):418-419. [2] Chen Wangqing, Zheng Rongshou, Baade PD, et al. Cancer statistics in China[J]. Ca Cancer Journal for Clinicians, 2016, 66(2):115-132. [3] Lambin P, Riosvelazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis.[J]. European Journal of Cancer, 2012, 48(4):441-446. [4] 张利文,刘侠,汪俊,等.基于定量影像组学的肺肿瘤良恶性预测方法[J].自动化学报,2017,43(12):2109-2114. [5] Wang Jun, Liu Xia, Dong Di, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method[J]. Acta Automatica Sinica, 2016:1272-1275. [6] Qi Zhang, Yang Xiao, Jing Fengsuo, et al. Sonoelastomics for breast tumor classification: A radiomics approach with clustering-based feature selection on sonoelastography.[J]. Ultrasound in Medicine & Biology, 2017, 43(5):1058-1069. [7] Spanhol FA, Oliveira LS, Petitjean C, et al. A dataset for breast cancer histopathological image classification[J]. IEEE Transactions on Biomedical Engineering: 2016, 63(7): 1455-1462. [8] Krizhevsky, A, Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks[C]// In Advances in neural infonnation processing systems, 2012:1097-1105 [9] Spanhol FA, Oliveira LS, Petitjean C, et al. Breast Cancer Histopathological Image Classification using Convolutional Neural Networks[C]// International Joint Conference on Neural Networks. IEEE,2016: 2560-2567. [10] Pham D, Watanabe Y, Higuchi M, et al. Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography [J]. Sci Rep, 2017, 7: 43209. [11] Cameron A, Khalvati F, Haider A, et al. MAPS: A quantitative radiomics approach for prostate cancer detection [J]. IEEE Trans Biomed Eng, 2016, 63(6): 1145-1156. [12] Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models[J]. BMC Medical Imaging, 2015, 15(1):27. [13] Tu Shuju Wang Chihwei, Pan Kuangtse, et al. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening[J]. Physics in Medicine & Biology, 2018, 63(6):065005. [14] Haralick RM. Statistical and structural approaches to texture[J]. Proceedings of the IEEE, 2005, 67(5):786-804. [15] Haralick RM, Shanmugam K, Dinstein I. Textural Features for Image Classification[J]. IEEE Transactions on Systems Man & Cybernetics, 1973, SMC-3(6):610-621. [16] Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(7):971-987. [17] Gabor D. Theory of communication[J]. IEE Proc London, 1946, 93(73):58-58. [18] Raiko T, Ilin A, Karhunen J. Principal component analysis for large scale problems with lots of missing values[C]// European Conference on Machine Learning. Berlin:Springer-Verlag, 2007:691-698. [19] Min R, Stanley DA, Yuan Z, et al. A Deep Non-linear Feature Mapping for Large-Margin kNN Classification[C]// 2009 Ninth IEEE International Conference on Data Mining. Washington: IEEE Computer Society, 2009:357-366. [20] Breiman L. Random forests, machine learning 45[J]. Journal of Clinical Microbiology, 2001, 2:199-228. [21] Huang Guangbin, Zhu Qinyu, Siew CK. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1):489-501. [22] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
