Magnetic resonance differential analysis for different hormone receptor expression status in HER-2-positive breast cancer

HER-2阳性乳腺癌不同激素受体表达状态的磁共振差异分析

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Abstract

OBJECTIVES: Currently, it is difficult to assess the expression status of hormone receptor (HR) in breast malignant tumors with human epidermal growth factor receptor 2 (HER-2)-positive in the early preoperative stage, and it is difficult to predict whether it is non-invasively. This study aims to explore the value of MRI on the different HR expression status (HR(+)/HR(-)) in HER-2 positive breast cancer. METHODS: Thirty patients with HR+ HER-2-positive breast cancer (HR+ group) and 23 patients with HR-HER-2-positive breast cancer (HR- group) from the First Hospital of Hunan University of Traditional Chinese Medicine between January 7, 2015 and November 26, 2021 were selected as subjects, and all the patients were examined by MRI and all were confirmed by surgery or pathological biopsy puncture. The immunohistochemical staining results were used as the gold standard to analyze the basic clinical conditions, peri-lesion conditions and MRI sign characteristics in the 2 groups. RESULTS: There were all significant differences in terms of mass margins, internal reinforcement features, and apparent diffusion coefficient (ADC) values between the HR+ group and the HR- group (all P<0.05). The logistic multivariate regression model showed that: when the lesion presented as a mass-type breast cancer on MRI, the internal enhancement features of the lesion were an independent predictor for differentiation in the 2 types of breast cancer [odds ratio (OR)=5.95, 95% CI: 1.223 to 28.951, P<0.05], and the mass margin (OR=0.386, 95% CI: 0.137 to 1.082, P>0.05) and ADC value (OR=0.234, 95% CI: 0.001 to 105.293, P>0.05) were not the independent predictors in distinguishing the 2 types of breast cancer. CONCLUSIONS: Multiparametric MRI has good diagnostic value for HR expression status in HER-2-positive breast cancer. Combined logistic regression analysis to construct a predictive model may be helpful to the identical diagnosis.

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