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中华医学超声杂志(电子版) ›› 2019, Vol. 16 ›› Issue (06) : 419 -425. doi: 10.3877/cma.j.issn.1672-6448.2019.06.003

所属专题: 乳腺超声 文献

浅表器官超声影像学

乳腺超声造影预测模型的建立及其对乳腺良恶性病变诊断效能的分析
赵璐1, 张莹2, 程颢3, 黄品同2,()   
  1. 1. 323000,丽水市人民医院超声科
    2. 310009,杭州,浙江大学医学院附属第二医院超声科
    3. 710062,西安交通大学附属省肿瘤医院超声科
  • 收稿日期:2018-09-26 出版日期:2019-06-01
  • 通信作者: 黄品同

Comparative study of a contrast-enhanced ultrasound predictive model and dynamic contrast-enhanced magnetic resonance imaging in diagnosis of breast lesions

Lu Zhao1, Ying Zhang2, Hao Cheng3, Pintong Huang2,()   

  1. 1. Department of Ultrasonography, Lishui People′s Hospital, Lishui 323000, China
    2. Department of Ultrasonography, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China
    3. Department of Ultrasonography, Shanxi Provincial Tumor Hospital, Affiliated Hospital of Medical College of Xi'an Jiaotong University, Xi′an 710062, China
  • Received:2018-09-26 Published:2019-06-01
  • Corresponding author: Pintong Huang
  • About author:
    Corresponding author: Huang Pintong, Email:
引用本文:

赵璐, 张莹, 程颢, 黄品同. 乳腺超声造影预测模型的建立及其对乳腺良恶性病变诊断效能的分析[J/OL]. 中华医学超声杂志(电子版), 2019, 16(06): 419-425.

Lu Zhao, Ying Zhang, Hao Cheng, Pintong Huang. Comparative study of a contrast-enhanced ultrasound predictive model and dynamic contrast-enhanced magnetic resonance imaging in diagnosis of breast lesions[J/OL]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2019, 16(06): 419-425.

目的

分析乳腺超声造影(CEUS)的增强特征,构建乳腺病变CEUS预测模型,探讨该模型对乳腺良恶性病变的诊断价值。

方法

选取2016年6月至2018年8月于丽水市人民医院及浙江大学医学院附属第二医院就诊的乳腺病变患者192例共195个病灶,所有病灶均为常规超声检查BI-RADS分类4类及以上,均经穿刺活检或手术取得病理结果。将病例分为CEUS组120例共123个病灶,均经CEUS检查;CEUS+动态增强磁共振(DCE-MRI)组72例共72个病灶,均接受CEUS及DCE-MRI检查。对CEUS组120例患者的CEUS模式特征进行单因素及多因素Logistic回归分析,筛选预测乳腺恶性病变的CEUS危险因素,并建立预测模型,绘制ROC曲线。以CEUS+DCE-MRI组72例患者的病理结果为"金标准",分别计算CEUS预测模型与DCE-MRI对乳腺良恶性病变的诊断效能。

结果

Logistic回归分析结果显示诊断乳腺恶性病灶的CEUS特征性表现为增强后病灶范围增大(OR=12.941,P=0.003),"蟹足"征或血管扭曲缠绕(OR=7.553,P=0.009),灌注缺损(OR=5.670,P=0.024)。建立的风险预测模型即Logistic回归方程为:Y=-4.108+2.560X6+2.022X7+1.735X8。该模型预测乳腺良恶性病灶的ROC曲线下面积为0.953。以穿刺或术后病理结果为"金标准",CEUS风险预测模型诊断乳腺良恶性病变的敏感度、特异度、阳性预测值、阴性预测值以及准确性分别为93.0%、73.3%、93.0%、73.3%、88.9%;DCE-MRI诊断乳腺良恶性病变的敏感度、特异度、阳性预测值、阴性预测值以及准确性分别为94.7%、73.3%、93.1%、78.6%、90.3%。CEUS风险预测模型与DCE-MRI诊断乳腺良恶性病变的一致性较高(Kappa值=0.70)。

结论

乳腺CEUS预测模型对鉴别良恶性病灶具有较高的诊断效能,且操作相对简单、检查时间短、可重复性好、价格相对低廉,不失为诊断乳腺良恶性病变的一种有效和可靠方法。

Objective

To identify the contrast-enhanced ultrasound (CEUS) features of the breast, to build a CEUS predictive model for breast lesions, and to evaluate the diagnostic value of this model in distinguishing breast benign from malignant lesions.

Methods

A total of 192 patients with 195 breast lesions were included in the study. The lesions were divided into two groups: 123 lesions in a CEUS group and 72 in a CEUS+ dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) group. In the CEUS group, CEUS were used to examine each lesion. Then, risky CEUS patterns in breast malignant lesions were identified by logistic regression analysis to build a breast CEUS predictive model. Using final pathology results as the gold standard, the diagnostic efficiencies of the CEUS prediction model and DCE-MRI were evaluated.

Results

Three independent variables, namely, increased lesion scope (OR=12.941), ″crab foot″ sign (OR=7.553), and filling defect (OR=5.670), were selected in the final step of the logistic regression analysis in the CEUS group. The CEUS predictive model was built as Y=-4.108+ 2.560X6+ 2.022X7+ 1.735X8. Using final pathology results as the gold standard, the area under ROC curve of the CEUS predictive model in distinguishing between benign and malignant breast lesions was calculated to be 0.953, and the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CEUS predictive model were 93.0%, 73.3%, 93.0%, 73.3%, and 88.9%, respectively; the corresponding values of DCE-MRI were 94.7%, 73.3%, 93.1%, 78.6%, and 90.3%. The consistency between the CEUS risk prediction model and DCE-MRI in the diagnosis of benign and malignant breast lesions was high (Kappa value=0.70).

Conclusion

The breast CEUS predictive model built here can predict the malignant risk of breast lesions more accurately. It is an effective and reliable method for the diagnosis of benign and malignant breast lesions because of its simple operation, short examination time, reproducibility, and relatively low price.

表1 评估乳腺良恶性病灶的超声造影模式单因素分析[个(%)]
图1 乳腺恶性病变超声造影图像。图a示造影特征为快进或同进伴高增强或等增强,出现滋养血管或"蟹足"征,不伴充盈缺损,病理提示为浸润性癌;图b示造影特征为快进高增强伴增强后病灶范围增大,病理提示浸润性导管癌;图c示造影特征为快进高增强,边界不清伴充盈缺损,增强后病灶范围无增大,病理提示浸润性导管癌
图2 乳腺良性病变超声造影图像。图a示造影特征为快进高增强或等增强,增强后病灶范围无增大,边界清楚,病理提示为纤维瘤;图b示造影特征为同进或慢进,等增强,增强后难以分辨边界及形态,病理提示纤维腺瘤并腺病;图c示造影特征为同进或慢进,增强后病灶范围缩小,病理提示为纤维瘤
表2 评估乳腺良恶性病变的超声造影增强模式赋值表
表3 评估乳腺良恶性病变的超声造影模式多因素Logistic回归分析结果
图3 超声造影预测模型诊断乳腺良恶性病变的ROC曲线
表4 超声造影预测模型与动态增强磁共振对乳腺良恶性病变的诊断结果
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