基于乳腺X线图像纹理特征建立机器学习模型在鉴别良恶性乳腺肿块中的价值

目的:探讨基于乳腺X线图像的纹理分析建立机器学习模型在鉴别乳腺肿块良恶性中的价值.方法:回顾性搜集经病理证实的124个乳腺良性肿块和139个乳腺恶性肿块的乳腺X线图像.并按照7﹕3的比例划将所有病灶随即分为训练集和验证集.使用MaZda软件,在X线图像上于乳腺病灶内手动勾画ROI,提取6类共133个纹理特征,经降维处理...

Application of machine learning model based on the texture features on mammography in the differeniation diagnosis of benign and malignant breast masses

Objective:The purpose of this study was to investigate the value of machine learning model based on the texture features of mammography image in the differentiation diagnosis of benign and malignant breast masses.Methods:Mammography images of 124 benign and 139 malignant breast masses were collected and analyzed retrospectively.All the masses were divided into two groups for training set and verification set according to the proportion of 7﹕3.The region of interest(ROI)was drawn,and 133 texture features of six types were extracted using MaZda software.After the extracted texture features were reduced in dimensionality,four models including linear discriminant analysis(LDA),logistic regression(LR),random forest(RF)and support vector machine(SVM)were obtained based on training set data;and verified in the verification set.The accuracy,Kappa coefficient,and AUC were used to evaluate the performance of the four models in the training and verification sets,and the AUC differences of the four models were analyzed by the delong test.Results:The accuracy,Kappa coefficient,and AUC of the RF model in the training and verification sets were higher than those of the other three models.In the verification set,the accuracy of the RF model was 94.9%,the Kappa coefficient was 0.896,and the AUC was 0.946,which was statistically different with the AUC value of LDA model and LR model(P<0.05).The accuracy and Kappa coefficient of SVM model were second only to RF model.The AUC of SVM model was higher than that of LDA and logistic regression model,but the difference was not statistically significant(P>0.05).Conclusion:The machine learning model based on the texture features of mammography image has certain advantages in differentiating benign and malignant breast masses.RF model showed better classification performance,and SVM model was second only to RF model.