Abstract
BackgroundHuman 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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.
Highlights
Breast cancer is a highly heterogenous tumor that has recently become the most common malignant tumor worldwide [1, 2]
The results demonstrated that clinical and ultrasound features were signifi‐ cantly associated with biomarkers
The results of this study suggested that, regarding the US features of breast cancers, Progesterone receptor (PR) status was mainly reflected by the posterior acoustic features, and HER2 status by the tumor shape and presence of calcifications
Summary
Breast cancer is a highly heterogenous tumor that has recently become the most common malignant tumor worldwide [1, 2]. HER2+ subtype (non-luminal) breast cancer is defined as ER-, PR-, HER2+, and has a high degree of malignancy and a poor prognosis, with a heterogeneous clinical and biological presentation. HER2 score 2 + on IHC require additional fluorescent in-situ hybridization (FISH) or chromogenic in-situ hybridization (CISH) testing to determine their status [7, 8] They adversely affect the diagnosis and treatment of HER2+ breast cancer. The diagnosis of breast cancer subtypes and biomarkers of breast cancer requires preoperative core-needle or postoperative pathology, which is an invasive and time-consuming process. If these could be obtained preoperatively and noninvasively, it would make the treatment process more timely, effective and precise. 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 estro‐ gen receptor, progesterone receptor, and human epidermal growth factor receptor
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