Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer

Abstract

Background: Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2.

Methods: We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity.

Results: The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p < 0.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p < 0.05). The area under receiver operating characteristic curve, accuracy, sensitivity, specificity of the model to predict human epidermal growth factor receptor2+ subtype breast cancer was 0.697, 60.14%, 72.46%, 58.49% and 0.725, 72.06%, 64.71%, 72.89% in the training and test sets, respectively.

Conclusions: Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

Keywords: Breast cancer; Estrogen receptor; Human epidermal growth factor receptor-2; Progesterone receptor; Ultrasound.

Fig. 1

The distribution of pathological types (A) and ER, PR, HER2 (B) status among all cases, training and test sets. In this paper, the pathological types of breast cancer were categorized into two types: ductal carcinoma, lobular carcinoma and other types. And the distribution and number of cases in the three groups are shown in (A). B shows the distribution and number of cases of ER, PR and HER2 expression status in the three groups. ER estrogen receptor, PR progesterone receptor, HER2 human epidermal growth factor receptor-2

Fig. 2

The receiver operating characteristic curve of the predictive model for the HER2 positive subtype in the training set. This figure demonstrates the predictive ability of the model combing clinical and ultrasound features for HER2+ subtype with an AUC of 0.697 in the training set. HER2 human epidermal growth factor receptor-2, AUC area under the receiver operating characteristic curve

Fig. 3

The receiver operating characteristic curve of the predictive model for the HER2 positive subtype in the test set. This figure shows that the model with combined clinical and ultrasound features had moderate predictive power for HER2+ subtype, with an AUC of 0.725 in the test set. HER2 human epidermal growth factor receptor-2, AUC area under the receiver operating characteristic curve