Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of BI-RADS 4 category breast lesions

WANG Xiangqian; XU Huaning; CAI Ting; WU Yiyun

doi:10.16245/j.cnki.issn1008-6978.2021.02.012

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Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of BI-RADS 4 category breast lesions

更新时间：2021-10-08

- Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of BI-RADS 4 category breast lesions
- Journal of Clinical Ultrasound in Medicine Vol. 23, Issue 2, Pages: 110-114(2021)
- 作者机构：
  
  南京中医药大学附属医院超声医学科
- 作者简介：
- 基金信息：
- DOI：10.16245/j.cnki.issn1008-6978.2021.02.012
  CLC： R737.9;R445.1
- Published：2021，
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[1]汪向前,许华宁,蔡婷,吴意赟.乳腺超声造影预测模型构建及对BI-RADS 4类病变的风险评估[J].临床超声医学杂志,2021,23(02):110-114.

WANG Xiangqian, XU Huaning, CAI Ting, et al. Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of BI-RADS 4 category breast lesions. [J]. Journal of Clinical Ultrasound in Medicine 23(2):110-114(2021)
[1]汪向前,许华宁,蔡婷,吴意赟.乳腺超声造影预测模型构建及对BI-RADS 4类病变的风险评估[J].临床超声医学杂志,2021,23(02):110-114. DOI： 10.16245/j.cnki.issn1008-6978.2021.02.012.

WANG Xiangqian, XU Huaning, CAI Ting, et al. Establishment of predictive models of contrast-enhanced ultrasound in the evaluation of BI-RADS 4 category breast lesions. [J]. Journal of Clinical Ultrasound in Medicine 23(2):110-114(2021) DOI： 10.16245/j.cnki.issn1008-6978.2021.02.012.

摘要

目的分析乳腺良恶性病变的超声造影特征

构建预测模型

探讨其对乳腺影像报告与数据系统（BI-RADS）4类病变风险的评估价值。方法选取我院均经手术病理结果证实的乳腺病变患者174例

共180个病灶（BI-RADS 4类）。将其分为模型构建样本（91例

共94个病灶）和模型验证样本（83例

共86个病灶）。对模型构建样本的超声造影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;绘制受试者工作特征（ROC）曲线分析该预测模型对模型验证样本乳腺良恶性病变的诊断效能。同时分析不同经验超声医师对模型验证样本判读结果的一致性。结果 Logistic回归分析显示

特征性危险因素为"蟹足"征或血管扭曲征、增强均匀性及增强后病灶范围扩大（OR=11.308、5.980、3.377

均P<0.01）。Logistic回归方程（即超声造影预测模型）:y=-4.239+2.425x8+1.788x3+1.217x5（x8:"蟹足"征或血管扭曲征;x3:增强均匀性;x5:增强后病灶范围）;该模型预测乳腺良恶性病变的ROC曲线下面积为0.909

敏感性、特异性、准确率、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%。不同经验超声医师判读结果一致性好

Kappa值为0.683。结论乳腺超声造影预测模型对BI-RADS 4类乳腺病变具有较高的风险评估价值

且不同经验超声医师间采用该预测模型鉴别诊断乳腺良恶性病变一致性好。

Abstract

Objective To analyze the features of contrast-enhanced ultrasound（CEUS） of benign and malignant breast lesions

construct a predictive model

and to explore the value of this model for risk assessment of breast imaging report and data system（BI-RADS） 4 category of lesions.Methods A total of 174 patients with a total of 180 lesions（BI-RADS 4 category）who underwent breast ultrasound examination in our hospital were selected.All patients obtained the final pathological results through surgery.The cases were divided into model construction group（91 cases with 94 lesions） and model verification group（83 cases with 86 lesions）.Single-factor and multi-factor Logistic regression analysis was performed on the characteristics of the CEUS enhancement mode of the model building group to construct a predictive model and draw the ROC curve.The diagnostic efficacy of the predictive model for benign and malignant breast lesions was calculated

and the consistency of the blind reading results was analyzed by ultrasound doctor with different experience.Results The characteristic risk factors of Logistic regression analysis were "crab foot" sign or vascular distortion sign

enhanced uniformity and enhanced focal area（OR=11.308

5.980

3.377

all P

0.01）.The Logistic regression equation（the predictive model）:y=-4.239+2.425 x

+1.788 x

+1.217 x

（x

: "crab foot" sign or vascular distortion sign

:enhanced uniformity

:enhanced focal area）.The area under ROC curve of the CEUS predictive model in distinguishing benign and malignant breast lesions was 0.909

the 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.The Kappa value of the blind reading result consistency test between the different ultrasound doctor was 0.683.Conclusion Breast CEUS prediction model has a high risk assessment value for BI-RADS 4 category of breast lesions

and different ultrasound doctor use this prediction model to diagnose benign and malignant breast lesions has high consistency.

关键词

超声检查造影剂乳腺肿瘤良恶性Logistic模型

Keywords

UltrasonographyContrast agentBreast tumorbenign and malignantLogistic models

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