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2026-10-09 星期五
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王禹翰,王博娟,徐景竹,张涛,王兴华.常规超声联合显微超声造影(SR CEUS)列线图鉴别乳腺影像报告和数据系统4类良、恶性病变[J].中国医学影像技术,2026,42(6):867~871

常规超声联合显微超声造影(SR CEUS)列线图鉴别乳腺影像报告和数据系统4类良、恶性病变

Conventional ultrasound combined with super resolution contrast-enhanced ultrasound (SR CEUS) nomogram for differentiating breast imaging reporting and data system category 4 benign and malignant lesions

投稿时间：2025-10-24 修订日期：2026-03-25

DOI：10.13929/j.issn.1003-3289.2026.06.012

中文关键词: 乳腺病变列线图超声检查显微超声造影乳腺影像报告和数据系统

英文关键词:breast lesions nomograms ultrasonography super resolution contrast-enhanced ultrasound BI-RADS

基金项目:

作者	单位	E-mail
王禹翰	山西医科大学医学影像学院, 山西太原 030001
王博娟	山西医科大学第二医院超声科, 山西太原 030013
徐景竹	山西医科大学第二医院超声科, 山西太原 030013
张涛	山西医科大学医学影像学院, 山西太原 030001
王兴华	山西医科大学第二医院超声科, 山西太原 030013	wangxhus@163.com

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中文摘要:

目的观察常规超声联合显微超声造影(SR CEUS)列线图鉴别乳腺影像报告和数据系统(BI-RADS)4类良、恶性病变的价值。方法回顾性分析180例BI-RADS 4类乳腺病变[包括62例良性(良性组)、118例恶性(恶性组)]术前常规超声与SR CEUS资料;比较组间SR CEUS参数差异,以单及多因素logistic回归分析筛选BI-RADS 4类恶性病变的独立预测因子,构建列线图模型并实施内部验证。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),并以校准曲线及决策曲线分析(DCA)评估模型效能。结果恶性组血管密度(VD)、血流容积(FWVD)、分形维数(FD)、灌注指数(PI)、平均速度(Mean Vel)及最小速度(Min Vel)均高于,而速度方差(Vel Var)与方向方差(Dir Var)均低于良性组(P均＜0.05)。病变最大径>2.0 cm(OR=2.753)、形状不规则(OR=10.561)、高回声晕(OR=16.367)、高VD(OR=1.053)及Min Vel(OR=1.248)均为恶性病变的独立预测因子(P均＜0.05);基于此构建的列线图模型的鉴别敏感度为86.44%、特异度为85.48%、准确率为86.11%,且其AUC(0.917)高于常规超声的0.836(Z=2.359,P=0.018);列线图模型校准度良好(P=0.934)且临床净收益较高。结论常规超声联合SR CEUS列线图能显著提升鉴别BI-RADS 4类良、恶性病变效能。

英文摘要:

Objective To observe the value of conventional ultrasound combined with super resolution contrast-enhanced ultrasound (SR CEUS) nomogram for differentiating breast imaging reporting and data system (BI-RADS) category 4 benign and malignant lesions. Methods Conventional ultrasound and SR CEUS data of 180 patients with BI-RADS 4 lesions, including 62 cases of benign (benign group) and 118 cases of malignant lesions (malignant group) were retrospectively analyzed, and SR CEUS parameters were compared between groups. The independent predictors of BI-RADS 4 malignant lesions were screened using univariate and multivariate logistic regression to construct a nomogram, which was then internally validated. The receiver operating characteristic (ROC) curves were drawn, the area under the curves (AUC) were calculated, while calibration curve, and decision curve analysis (DCA) were used to evaluate performance of the model. Results Vessel density (VD), flow-weighted vessel density (FWVD), fractal dimension (FD), perfusion index (PI), the mean velocity (Mean Vel) and the minimum velocity (Min Vel) in malignant group were all higher, while velocity variance (Vel Var) and directional variance (Dir Var) were lower than those in benign group (all P＜0.05). The maximum diameter of the lesion>2.0 cm (OR=2.753), irregular shape (OR=10.561), hyperechoic halo (OR=16.367), higher VD (OR=1.053) and Min Vel (OR=1.248) were all independent predictors of malignancy (all P＜0.05). The sensitivity, specificity and accuracy of this nomogram model were 86.44%, 85.48% and 86.11%, respectively, and its AUC was 0.917, higher than the AUC of conventional ultrasound (0.836, Z=2.359, P=0.018). The calibration of the model was good (P=0.934), and the model had high clinical net benefit. Conclusion Conventional ultrasound combined with SR CEUS nomogram could significantly improve efficiency for differentiating BI-RADS 4 benign and malignant lesions.

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