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-RADS 3-5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.