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Chinese Journal of Medical Ultrasound (Electronic Edition) ›› 2025, Vol. 22 ›› Issue (12): 1130-1139. doi: 10.3877/cma.j.issn.1672-6448.2025.12.005

• Superficial Parts Ultrasound • Previous Articles    

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 Online:2025-12-01 Published:2026-04-03
  • Contact: Xinghua Wang

Abstract:

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.

Key words: Breast cancer, Human epidermal growth factor receptor 2, Contrast-enhanced ultrasound, Super resolution imaging, Nomogram

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