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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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>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\u2009\u00b1\u2009, Group II: progesterone receptor\u2009\u00b1\u2009and Group III: human epidermal growth factor receptor2\u2009\u00b1\u2009. 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>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\u2009\u2264\u200950\u00a0years was an independent predictor of progesterone receptor\u2009+\u2009(p\u2009=\u20090.007), and posterior enhancement was a negative predictor of progesterone receptor\u2009+\u2009(p\u2009=\u20090.013) 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\u2009=\u20090.001, p\u2009=\u20090.007, p\u2009=\u20090.010, p\u2009&lt;\u20090.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p\u2009&lt;\u20090.05). 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-021-00714-0","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:04:36Z","timestamp":1638453876000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer"],"prefix":"10.1186","volume":"21","author":[{"given":"Jin","family":"Zhou","sequence":"first","affiliation":[]},{"given":"An-qi","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Shi-chong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Jia-wei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wen-xiang","family":"Zhi","sequence":"additional","affiliation":[]},{"given":"Yun-xia","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Lang","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Jiong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Cai","family":"Chang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"issue":"1","key":"714_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21590","volume":"70","author":"RL Siegel","year":"2020","unstructured":"Siegel RL, Miller KD, Jemal A. 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