切换至 "中华医学电子期刊资源库"

中华医学超声杂志(电子版) ›› 2025, Vol. 22 ›› Issue (12) : 1130 -1139. doi: 10.3877/cma.j.issn.1672-6448.2025.12.005

浅表器官超声影像学

临床参数和二维超声特征联合超声显微造影征象构建的列线图模型预测HER-2阳性乳腺癌的应用价值
王禹翰1,2, 王博娟2, 徐景竹2, 张涛1,2, 王兴华2,()   
  1. 1 030001 太原,山西医科大学医学影像学院
    2 030013 太原,山西医科大学第二医院超声科
  • 收稿日期:2025-09-28 出版日期:2025-12-01
  • 通信作者: 王兴华
  • 基金资助:
    山西省基础研究计划资助项目(202403021221318)

A nomogram integrating clinical data, B-mode ultrasound features, and super-resolution contrast-enhanced ultrasound characteristics to predict HER-2 positive breast cancer

Yuhan Wang1,2, Bojuan Wang2, Jingzhu Xu2, Tao Zhang1,2, Xinghua Wang2,()   

  1. 1 College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, China
    2 Department of Ultrasound, Second Hospital of Shanxi Medical University, Taiyuan 030001, China
  • Received:2025-09-28 Published:2025-12-01
  • Corresponding author: Xinghua Wang
引用本文:

王禹翰, 王博娟, 徐景竹, 张涛, 王兴华. 临床参数和二维超声特征联合超声显微造影征象构建的列线图模型预测HER-2阳性乳腺癌的应用价值[J/OL]. 中华医学超声杂志(电子版), 2025, 22(12): 1130-1139.

Yuhan Wang, Bojuan Wang, Jingzhu Xu, Tao Zhang, Xinghua Wang. A nomogram integrating clinical data, B-mode ultrasound features, and super-resolution contrast-enhanced ultrasound characteristics to predict HER-2 positive breast cancer[J/OL]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2025, 22(12): 1130-1139.

目的

探讨联合临床参数、二维超声特征及超声显微造影(SR-CEUS)征象构建的列线图模型预测人表皮生长因子受体2(HER-2)阳性乳腺癌的价值。

方法

收集2025年3月至9月就诊于山西医科大学第二医院经病理确诊为浸润性乳腺癌的132例患者,包括HER-2阳性患者55例,HER-2阴性患者77例。所有患者术前均行二维超声及SR-CEUS检查。经单因素及多因素Logistic回归分析筛选预测HER-2状态的影响因子。基于筛选结果,构建3个预测模型:临床参数结合二维超声特征模型(模型1)、SR-CEUS征象独立模型(模型2)及三者联合模型(模型3),并依据联合模型绘制列线图。采用Bootstrap法对列线图模型进行内部验证。采用受试者操作特征(ROC)曲线评估各模型预测效能,通过Delong检验比较模型间曲线下面积(AUC)的差异;利用校准曲线及Hosmer-Lemeshow检验评估列线图模型校准度与拟合优度,并运用临床决策曲线分析(DCA)评估模型临床决策获益情况。

结果

单因素回归分析显示,雌激素受体(ER)、孕激素受体(PR)、钙化、灌注缺损、血管密度、血流容积、灌注指数及血流速度均与HER-2状态显著相关(OR=0.174、0.149、3.399、2.837、1.085、1.104、1.231、1.081,P均<0.05)。多因素回归分析显示PR(OR=0.211)、钙化(OR=2.553)、灌注缺损(OR=3.469)、血流容积(OR=1.088)及灌注指数(OR=1.184)为预测乳腺癌HER-2状态的最佳组合。ROC曲线分析显示,模型1、模型2及模型3的AUC分别为0.763、0.832、0.880。联合模型AUC显著高于模型1(P<0.001)、模型2(P=0.039)。内部验证显示联合模型稳定性良好;校准曲线及Hosmer-Lemeshow检验表明联合模型校准度与拟合度良好;DCA证实联合模型在较宽的阈值范围内具有更高的临床净获益。

结论

基于临床参数、二维超声特征及SR-CEUS征象构建的联合列线图模型可有效预测HER-2阳性乳腺癌。

Objective

To evaluate the utility of a nomogram model integrating clinical parameters, B-mode ultrasound features, and super-resolution contrast-enhanced ultrasound (SR-CEUS) characteristics for predicting human epidermal growth factor receptor 2 (HER-2) positive breast cancer.

Methods

We enrolled 132 patients with pathologically confirmed invasive breast cancer (55 HER-2 positive, 77 HER-2 negative) from the Second Hospital of Shanxi Medical University between March and September 2025. All patients underwent preoperative B-mode ultrasound and SR-CEUS examinations. Predictors were screened through univariate and multivariate logistic regression analyses. Three predictive models were constructed: Model 1 (clinical parameters + B-mode ultrasound features), Model 2 (SR-CEUS characteristics alone), and Model 3 (clinical parameters + B-mode ultrasound features + SR-CEUS characteristics). A nomogram was developed based on Model 3. Internal validation was performed using the bootstrap method. Model performance was assessed via receiver operating characteristic (ROC) curves, with area under the curve (AUC) differences compared using DeLong test. Calibration curves and the Hosmer-Lemeshow test were used to evaluate calibration and goodness-of-fit, while decision curve analysis (DCA) was performed to quantify clinical decision benefit.

Results

Univariate analysis revealed significant associations between HER-2 status and estrogen receptor (ER), progesterone receptor (PR), calcification, perfusion defects, vascular density, flow weighted vessel density (FWVD), perfusion index (PI), and blood flow velocity (OR=0.174, 0.149, 3.399, 2.837, 1.085, 1.104, 1.231, 1.081, all P<0.05). Multivariate analysis identified PR negativity (odds ratio [OR]=0.211), calcification (OR=2.553), perfusion defects (OR=3.469), FWVD (OR=1.088), and PI (OR=1.184) as optimal predictors. The AUC values for Models 1, 2, and 3 in predicting HER-2 positive breast cancer were 0.763, 0.832, and 0.880, respectively. The combination model (Model 3) demonstrated a significantly higher AUC than Model 1 (P<0.001) and Model 2 (P=0.039). Internal validation confirmed model stability. Calibration curves and the Hosmer-Lemeshow test indicated good calibration and fit. DCA showed superior net clinical benefit across a wide threshold probability range for the combination model.

Conclusion

The nomogram integrating clinical parameters, B-mode ultrasound features, and SR-CEUS characteristics effectively predicts HER-2 positive breast cancer.

表1 2组乳腺癌患者临床参数、二维超声特征及超声显微造影征象对比
参数 HER-2阴性组(n=77) HER-2阳性组(n=55) 统计值 P
年龄(岁,
±s
56.21±11.30 55.85±10.22 t=0.187 0.852
身高[cm,MQ1Q3)] 160.00(157.00,162.00) 160.00(157.00,161.50) Z=-0.210 0.833
体质量[kg,MQ1Q3)] 60.00(57.00,70.00) 60.00(57.00,65.00) Z=-0.852 0.394
月经史[例(%)] χ2=0.157 0.692
绝经 50(64.94) 33(60.00)
正常 27(35.06) 22(40.00)
ER[例(%)] χ2=14.376 <0.001
阴性 8(10.39) 22(40.00)
阳性 69(89.61) 33(60.00)
PR[例(%)] χ2=22.858 <0.001
阴性 15(19.48) 34(61.82)
阳性 62(80.52) 21(38.18)
Ki-67[例(%)] χ2=0.842 0.359
阴性 31(40.26) 17(30.91)
阳性 46(59.74) 38(69.09)
淋巴结转移[例(%)] χ2=0.174 0.677
46(59.74) 30(54.55)
31(40.26) 25(45.45)
最大径[例(%)] χ2=0.786 0.375
≤2.0 cm 45(58.44) 27(49.09)
>2.0 cm 32(41.56) 28(50.91)
边界[例(%)] χ2=0.022 0.881
清晰 33(42.86) 22(40.00)
不清晰 44(57.14) 33(60.00)
形状[例(%)] - 0.649
规则 2(2.60) 3(5.45)
不规则 75(97.40) 52(94.55)
内部回声[例(%)] χ2=0.000 1.000
均匀 30(38.96) 21(38.18)
不均匀 47(61.04) 34(61.82)
后方回声[例(%)] χ2=0.796 0.372
无改变 66(85.71) 43(78.18)
衰减 11(14.29) 12(21.82)
钙化[例(%)] χ2=9.533 0.002
59(76.62) 27(49.09)
18(23.38) 28(50.91)
方位[例(%)] χ2=0.181 0.670
平行 57(74.03) 38(69.09)
不平行 20(25.97) 17(30.91)
周围回声改变[例(%)] χ2=0.217 0.642
28(36.36) 17(30.91)
49(63.64) 38(69.09)
灌注方式[例(%)] χ2=0.828 0.363
离心/弥散 25(32.47) 13(23.64)
向心 52(67.53) 42(76.36)
灌注均匀性[例(%)] χ2=1.477 0.224
均匀 14(18.18) 5(9.09)
不均匀 63(81.82) 50(90.91)
SR-CEUS形状[例(%)] χ2=0.009 0.923
规则 22(28.57) 17(30.91)
不规则 55(71.43) 38(69.09)
灌注后范围扩大[例(%)] χ2=0.094 0.760
18(23.38) 15(27.27)
59(76.62) 40(72.73)
灌注缺损[例(%)] χ2=6.388 0.011
36(46.75) 13(23.64)
41(53.25) 42(76.36)
血管密度[%,MQ1Q3)] 63.64(54.77,75.05) 79.62(71.78,85.47) Z=5.331 <0.001
血流容积(
±s
23.67±9.23 32.12±9.83 t=4.993 <0.001
分形维数(
±s
1.63±0.11 1.66±0.08 t=1.751 0.082
灌注指数[MQ1Q3)] 7.48(5.50,9.28) 10.93(8.52,15.10) Z=5.430 <0.001
血流速度[mm/s,MQ1Q3)] 11.03(9.03,14.12) 13.40(11.35,19.84) Z=3.226 0.001
速度方差[MQ1Q3)] 23.58(14.30,54.76) 24.92(14.53,42.00) Z=0.399 0.690
方向方差[MQ1Q3)] 1673.73(1314.24,2025.10) 1322.50(990.00,1746.46) Z=-2.532 0.011
表2 人表皮生长因子受体2阳性乳腺癌患者临床、二维超声及SR-CEUS各参数单因素及多因素Logistic回归分析结果
图1 预测人表皮生长因子受体2阳性乳腺癌的联合列线图模型
表3 3种模型预测乳腺癌人表皮生长因子受体2状态的诊断效能
图2 3种模型预测人表皮生长因子受体2状态的受试者操作特征曲线 注:AUC为曲线下面积
图3 患者女性,67岁,右乳浸润性乳腺癌,病理结果示人表皮生长因子受体2(HER-2)阳性、PR阴性。图a:右乳二维超声图像示病灶内部可见钙化;图b:SR-CEUS示病灶中央可见灌注缺损区;图c:定量参数血流容积(FWVD)均值为43.34、灌注指数(PI)均值为24.68;对照列线图模型PR得分27,钙化得分15,灌注缺损得分20,FWVD得分60,PI得分68,总得分190,提示HER-2阳性概率>0.9,与实际病理结果相一致
图4 联合模型预测乳腺癌患者人表皮生长因子受体2状态的校准曲线
图5 3种模型预测乳腺癌患者人表皮生长因子受体2状态的临床决策曲线
表4 超声显微造影各参数一致性分析的组内相关系数
1
Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025 [J]. CA Cancer J Clin, 2025, 75(1): 10-45.
2
Lüönd F, Tiede S, Christofori G. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression [J]. Br J Cancer, 2021, 125(2): 164-175.
3
He M, Zhao W, Wang P, et al. Efficacy and safety of Trastuzumab Emtansine in treating human epidermal growth factor receptor 2-positive metastatic breast cancer in Chinese population: a real-world multicenter study [J]. Front Med (Lausanne), 2024, 11: 1383279.
4
Miglietta F, Razeti MG, Caltavituro A, et al. The evolving landscape of hormone receptor-positive/HER2-negative metastatic breast cancer (EVOLVE): An Italian Delphi consensus report [J]. Crit Rev Oncol Hematol, 2025, 213: 104793.
5
Quan MY, Huang YX, Wang CY, et al. Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status [J]. Front Endocrinol (Lausanne), 2023, 14: 1144812.
6
Adrada BE, Candelaria R, Moulder S, et al. Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer [J]. Cancer, 2021, 127(16): 2880-2887.
7
康佳, 吴桐, 张蕾, 等. 不同分子分型乳腺癌的多模态超声特征和临床病理对照研究 [J]. 中华超声影像学杂志, 2020, 29(4): 330-336.
8
应育娟, 郑元义, 蔡晓军. 超分辨率超声微血流成像研究进展 [J]. 中国医学影像技术, 2021, 37(3): 462-465.
9
钟传钰, 郑元义. 超声超分辨率微血流成像研究进展 [J]. 中国医学影像技术, 2021, 37(12): 1799-1805.
10
Wolff AC, Somerfield MR, Dowsett M, et al. Human epidermal growth factor receptor 2 testing in breast cancer: ASCO-College of American pathologists guideline update [J]. J Clin Oncol, 2023, 41(22): 3867-3872.
11
Choong GM, Cullen GD, O'Sullivan CC. Evolving standards of care and new challenges in the management of HER2-positive breast cancer [J]. CA Cancer J Clin, 2020, 70(5): 355-374.
12
Meattini I, Bicchierai G, Saieva C, et al. Impact of molecular subtypes classification concordance between preoperative core needle biopsy and surgical specimen on early breast cancer management: single-institution experience and review of published literature [J]. Eur J Surg Oncol, 2017, 43(4): 642-648.
13
Seferina SC, Nap M, van den Berkmortel F, et al. Reliability of receptor assessment on core needle biopsy in breast cancer patients [J]. Tumour Biol, 2013, 34(2): 987-994.
14
Tamaki K, Sasano H, Ishida T, et al. Comparison of core needle biopsy (CNB) and surgical specimens for accurate preoperative evaluation of ER, PgR and HER2 status of breast cancer patients [J]. Cancer Sci, 2010, 101(9): 2074-2079.
15
Niikura N, Tomotaki A, Miyata H, et al. Changes in tumor expression of HER2 and hormone receptors status after neoadjuvant chemotherapy in 21,755 patients from the Japanese breast cancer registry [J]. Ann Oncol, 2016, 27(3): 480-487.
16
Hou Y, Nitta H, Wei L, et al. HER2 intratumoral heterogeneity is independently associated with incomplete response to anti-HER2 neoadjuvant chemotherapy in HER2-positive breast carcinoma [J]. Breast Cancer Res Treat, 2017, 166(2): 447-457.
17
Fan Y, Wang Y, He L, et al. Clinical features of patients with HER2-positive breast cancer and development of a nomogram for predicting survival [J]. ESMO Open, 2021, 6(4): 100232.
18
Xu A, Chu X, Zhang S, et al. Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma [J]. BMC Cancer, 2022, 22(1): 872.
19
Zhou L, Wu Y, Wen X, et al. Prediction model for assessing HER2 status patient with invasive ductal carcinoma based on clinical parameters and ultrasound features: a dual-center study [J]. BMC Women's Health, 2025, 25(1): 291.
20
Chen J, Yin Y, Li G, et al. Integrated nomogram to predict HER2 expression in breast tumor: clinical, ultrasound, and photoacoustic imaging approaches [J]. Eur J Cancer, 2024, 209: 114259.
21
Xu M, Zeng S, Li F, et al. Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer [J]. Radiol Med, 2024, 129(1): 29-37.
22
郑茵, 蒋添, 闫玉琪, 等. 超声在乳腺癌分子分型预测中的研究进展 [J]. 肿瘤影像学, 2025, 34(1): 86-91.
23
Errico C, Pierre J, Pezet S, et al. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging [J]. Nature, 2015, 527(7579): 499-502.
24
Betzig E, Patterson GH, Sougrat R, et al. Imaging intracellular fluorescent proteins at nanometer resolution [J]. Science, 2006, 313(5793): 1642-1645.
25
Hess ST, Girirajan TPK, Mason MD. Ultra-high resolution imaging by fluorescence photoactivation localization microscopy [J]. Biophys J, 2006, 91(11): 4258-4272.
26
Rust MJ, Bates M, Zhuang X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM) [J]. Nat Methods, 2006, 3(10): 793-795.
27
Hasan R, Bhatt D, Khan S, et al. Association of Her-2 expression and clinicopathological parameters in colorectal carcinoma in indian population [J]. Open Access Maced J Med Sci, 2018, 7(1): 6-11.
28
Yuan Z, Li Y, Zhang S, et al. Extracellular matrix remodeling in tumor progression and immune escape: From mechanisms to treatments [J]. Mol Cancer, 2023, 22(1): 48.
29
Casas-Arozamena C, Otero-Cacho A, Carnero B, et al. Haemodynamic-dependent arrest of circulating tumour cells at large blood vessel bifurcations as new model for metastasis [J]. Sci Rep, 2021, 11(1): 23231.
30
Kondoh M, Ohga N, Akiyama K, et al. Hypoxia-induced reactive oxygen species cause chromosomal abnormalities in endothelial cells in the tumor microenvironment [J]. PLoS One, 2013, 8(11): e80349.
31
Lei YM, Liu C, Lei BS, et al. The value of multimodal ultrasound imaging in differentiating HER2-positive breast cancer [J]. Front Oncol, 2026, 16: 1718785.
[1] 王晶, 毛丽娟, 王丽璠, 张雅琴, 赵崇克, 朱宇莉, 李翠仙, 徐辉雄, 韩红. 超分辨超声识别肿瘤血流特征对胆囊息肉样病变的鉴别诊断价值[J/OL]. 中华医学超声杂志(电子版), 2025, 22(11): 1036-1045.
[2] 陈子为, 邹南鑫, 宋禄达, 姜波, 张奥, 张夕, 李秋洋. 基于超声的Bosniak分级对肾脏囊性病变的诊断价值及一致性分析[J/OL]. 中华医学超声杂志(电子版), 2025, 22(11): 1062-1070.
[3] 曹俊杰, 姚志超, 曾宇琪, 郑进, 鄂一民, 谭梓仪, 周大勇, 张丽丽. 基于超声造影的颈动脉斑块易损性评估及卒中风险预测模型构建[J/OL]. 中华医学超声杂志(电子版), 2025, 22(11): 1071-1079.
[4] 潘辰蕊, 杨冰洁, 沈会明, 王颖彦, 韩佳豪, 李嘉. 多模态超声联合免疫炎症指标预测乳腺癌腋窝淋巴结转移的价值[J/OL]. 中华医学超声杂志(电子版), 2025, 22(10): 969-975.
[5] 仲洋杨, 邓舒瑶, 李永杰, 李庄. 男性隐匿性乳腺癌一例[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 60-63.
[6] 赵珂欣, 王蓉, 张钧, 杨哲, 石钰环, 姬雅楠, 刘敏丽, 张生军. 雷公藤提取物在三阴性乳腺癌中的作用机制[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(01): 55-59.
[7] 严一杰, 张军, 孟繁杰, 关志宇. 肺结节-胸膜关系预测CT引导下肺穿刺活检后气胸风险的作用研究[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(01): 36-41.
[8] 杜东莲, 史志伟, 姚洁, 张树敏, 代卫斌. 基于CT影像组学和临床特征预测单发肺结节生长的临床意义[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(01): 56-61.
[9] 邢颖, 王峰. 基于机器学习构建肝切除术后肝衰竭预测列线图模型及其预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(02): 190-196.
[10] 李鹏, 张维桢, 武国帅, 马伊凡, 张灵强. 超声造影在胰腺疾病中的应用研究进展[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(01): 119-123.
[11] 孙娟华, 白引苗, 孔胜男, 王梦雪, 王文慧, 张红梅. 胰腺癌患者化疗相关性恶心呕吐风险列线图构建及验证[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(02): 114-119.
[12] 张维娜, 潘亚娟, 徐敏. 晚期消化系统癌症手术患者器官/腔隙感染的风险预测模型的建立[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(02): 120-124.
[13] 慈娟娟, 吴俊成, 朱琴琴. 肝硬化合并食管胃静脉曲张破裂出血患者内镜治疗后再出血风险列线图的构建与验证[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(01): 47-52.
[14] 赵莉, 张敏伟, 郭浩东, 于海侠, 王光明, 李德春. 乳腺X线模型构建列线图预测乳腺癌HR及HER2的表达[J/OL]. 中华临床医师杂志(电子版), 2025, 19(10): 747-757.
[15] 王超, 张晓会, 李晓帆, 赵海丹. 维持性血液透析患者感染与心脑血管死亡风险的比较及联合预测模型构建[J/OL]. 中华临床医师杂志(电子版), 2025, 19(09): 675-681.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?