| 摘要: |
| 目的 分析乳腺良恶性病变的超声造影(CEUS)特征,构建预测模型,并探讨该模型对乳腺影像报告与数据系统(BI-RADS)4类病变的风险评估价值。方法 选取2018年9月至2020年3月在江苏省中医院接受乳腺超声造影检查的病人174例,共180个病灶。所有患者均通过外科手术取得最终病理结果。将病例分为模型构建组(91例共94个病灶)及模型验证组(83例共86个病灶)。对模型构建组CEUS增强模式特征进行单因素及多因素Logistic回归分析,构建出预测模型,绘制ROC曲线。以模型验证组病人的病理结果为“金标准”,计算该预测模型对乳腺良恶性病变的诊断效能。 结果 Logistic 回归分析特征性危险因素为“蟹足”征或血管扭曲征(OR=11.308,P＜0.001),增强均匀性(OR=5.980,P=0.006)及增强后病灶范围扩大(OR=3.377,P=0.001)。Logistic 回归方程(即构建出预测模型)为y=-4.239+2.4258+1.7883+1.2175。该模型预测乳腺良恶性病灶的ROC曲线下面积为0.909。鉴别诊断乳腺良恶性病灶的敏感度、特异度、准确性、阳性预测值及阴性预测值分别为95.83%、81.58%、89.53%、86.79%及93.94%。结论 乳腺超声造影预测模型对BI-RADS 4类乳腺病灶具有较高的风险评估价值。 |
| 关键词: 超声检查 造影剂 乳腺疾病 Logistic 模型 |
| DOI： |
| 投稿时间：2020-08-12修订日期：2020-09-08 |
| 基金项目:江苏省第五期“333高层次人才培养工程”(LGY2018063)；江苏省“六大人才高峰”第十二批项目(WSN-053)；南京中医药大学附属医院院级面上项目(Y20043)作者单位：210029 南京市,南京中医药大学附属医院超声医学科通讯作者：吴意赟,Email:wuyi425@sina.com |
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| Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of breast imaging reporting and data system 4 breast lesions |
| WANG Xiangqian,WU Yiyun,XU Huaning,CAI Ting |
| () |
| Abstract: |
| ABSTRACT Objective To analyze the contrast-enhanced ultrasound (CEUS) features of the benign and malignant breast lesions, to build a CEUS predictive model for exploring it’s value in the risk assessment of BI-RADS 4 breast lesions. Methods 174 patients with 180 lesions underwent contrast-enhanced ultrasound (CEUS) in the Affiliated hospital of nanjing university of Chinese medicine from September 2018 to March 2020 were selected. With final pathology results as the gold standard.The patients were divided into the model-Established group (91 cases with 94 lesions) and the model validation group (83 cases with 86 lesions).Univariate and multivariate Logistic regression analysis was carried out on the features of CEUS patterns in the model-Established group to construct a prediction model and draw ROC curve. Using final pathology results as the gold standard, the diagnostic efficacy of the CEUS predictive model was evaluated in the model validation group.Results Three independent variables of Logistic regression analysis were "crabfoot" or vascular distortion (OR=11.308, P < 0.001), enhanced homogeneity (OR=5.980, P=0.006) and increased lesion scope (OR=3.377, P=0.001). The CEUS predictive model was built as y=-4.239+2.425x8+1.788x3+1.217x5.The area under ROC curve of the CEUS predictive model in distinguishing between benign and malignant breast lesions was calculated to be 0.909, and the diagnostic sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the CEUS predictive model were 95.83%, 81.58%, 89.53%, 86.79% and 93.94%, respectively.Conclusion The breast CEUS predictive model can predict malignant risk of breast lesions more accurately and has high risk evaluation of BI-RADS 4 breast lesions.
KEY WORDS Ultrasonography;Contrast medium;Breast diseases;Logistic models. |
| Key words: Ultrasonography Contrast medium Breast diseases Logistic models. |
