基于MRI影像组学的BI-RADS 3-5类乳腺病变三分类

Classification of BI-RADS 3-5 Breast Lesions Based on MRI Radiomics

医生根据磁共振影像征象对患者的乳腺病变程度进行BI-RADS分类评估时存在一定的主观性,且BI-RADS 3-5类病变的良恶性存在交叉,在临床诊断时极易发生因诊断类别较高而造成不必要的有创治疗.针对这些问题,本文应用影像组学技术对乳腺的T1加权(T1W)和动态对比增强(DCE)磁共振图像进行特征提取和融合,采用最小绝对收缩和选择算子(LASSO)算法筛选出各特征集的最优特征集,并分别使用支持向量机(SVM)、随机森林(RF)、K最近邻(KNN)及逻辑回归(LR)算法进行BI-RADS 3-5类乳腺病变三分类,并且在此基础上实现乳腺良恶性分类.结果显示基于特征融合的四个影像组学模型对乳腺病变BI-RADS3-5类的分类准确率分别为81.25%、87.50%、78.38%、81.25%;对乳腺病变良恶性鉴别的准确率分别为90.91%、93.55%、92.73%、94.55%.这表明MRI影像组学结合机器学习的算法对乳腺病变BI-RADS分类效果及良恶性鉴别效果均较好,且特征融合可进一步提高分类预测的准确率.

The classification of the breast imaging-reporting and data system(BI-RADS)based on magnetic resonance imaging(MRI)refers to the classification of the degree of lesions according to the image signs of lesions,which is usually subjective.Moreover,the benign and malignant lesions of BI-RADS 3-5 are overlapping,which is prone to unnecessary invasive treatment due to high diagnostic categories in clinical diagnosis.To address these problems,this research applied radiomics for feature extraction and fusion of T1-weighted(T1W)and dynamic contrast-enhanced(DEC)MRI.The least absolute shrinkage and selection operator(LASSO)algorithm was used to screen out the optimal feature collection of each type of MR image.Support vector machine(SVM),random forest(RF),K-nearest neighbour(KNN)and logistic regression(LR)algorithms were applied for BI-RADS 3-5 classification,based on which the benign and malignant lesions were further classified.The results showed that the classification accuracy of breast BI-RADS 3-5 by four radiomics models based on feature fusion was 81.25%,87.50%,78.38%,and 81.25%,respectively.Their accuracy in distinguishing the benign and malignant breast lesions was 90.91%,93.55%,92.73%,and 94.55%,respectively.This indicates that the combination of radiomics and machine learning correlation algorithm has a good effect on breast MRI BI-RADS classification and benign and malignant differentiation,and feature fusion can further improve the accuracy of classification prediction.