摘要目的 探讨鉴别乳腺良恶性病灶的客观评分方法,为乳腺团块型病灶的良恶性判断提供客观评价依据.方法 将第4版乳腺影像和报告系统(BI-RADS)的MR部分对团块型病灶的描述作为病灶评价指标对188个高血供团块型病灶评分,根据评分结果与病理结果进行ROC曲线分析,计算曲线下面积并计算最佳良恶性诊断阈值；根据最佳诊断阈值及病灶良恶性倾向将病灶归入Ⅰ～Ⅴ级(Ⅰ级阴性、Ⅱ级良性、Ⅲ级可能良性、Ⅳ级可疑恶性、Ⅴ级高度提示恶性),最后检测评分方法诊断恶性团块型病灶的敏感度和特异度.结果 病理证实188个高血供病灶中有91个恶性,97个良性.ROC曲线下面积为0.938±0.016,评分法鉴别病灶良恶性的最佳阈值为5分.病灶分级结果为Ⅱ级24例、Ⅲ级72例、Ⅳ级54例、Ⅴ级38例.评分法诊断恶性乳腺团块型病灶的敏感度为87.91％,特异度为87.62％.除去Ⅲ级中3例假阴性的导管原位癌,特异度提高到90.90％.结论 建立在多参数标准化分析基础上的评分方法有助于客观判读乳腺动态增强MR影像.

abstractsObjective The purpose of this paper is to grade hypervascularized mass lesions in CE MR imaging of the breast by analyzing morphology and dynamic characteristics.Methods Criteria for the evaluation of breast MR findings were used,including the analysis of shape,margin,internal enhancement characteristics,initial enhancement phase,and delayed phase enhancement pattern.A total of 188 hypervascularized mass lesions were analyzed,and scores were given from 1 to 10 points and then the mass was classified into one of five categories.Next,the score results and pathology results were used to calculate the ROC curve and to find out the optimal benign and malignant diagnosis points.According to these results,the lesions were classified as benign or malignant.The sensitivity and specificity of this classification system were also calculated.Results Out of the 188 hypervascularized mass lesions,91 lesions were confirmed to be malignant by pathologic diagnosis and 97 to be benign.The area under the ROC curve was 0.938 ± 0.016.This curve was used to calculate the optimal point of differentiation diagnosis,and it was found to be a score of 5.The 188 lesions were classified into grade Ⅱ of 24 lesions,grade Ⅲ of 72,grade Ⅳ of 54,and grade Ⅴ of 38.Sensitivity for the detection of malignancy using this classification system was 87.91％ and specificity was 87.62％.If the three cases with false negative results of ductal carcinoma in situ of grade Ⅲ were excluded,the specificity increased to 90.90％.Conclusion The classification based on a multifactorial analysis is very helpful in the objective interpretation of breast CE MRI.