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associated with human epidermal growth factor receptor type 2 (HER2) 2+ status of breast cancer. Materials and methods 92 MRI cases including 52 HER2 2+ positive and 40 negative patients confirmed by fluorescencein situhybridization were retrospectively selected. The lesion area was semi-automatically delineated, and a total of 488 texture features were respectively extracted from precontrast, postcontrast, and subtraction images. The Student'st-test or Mann-WhitneyUtest was performed to identify statistically significant features between different HER2 2+ amplification groups. Least absolute shrinkage and selection operator (LASSO) was used to search for the optimal feature subsets. Three machine learning classifiers, logistic regression analysis (LRA), quadratic discriminant analysis (QDA), and support vector machine (SVM), were used with a leave-one-out cross validation method to establish the classification models of HER2 2+ status. Classification performance was evaluated by receiver operating characteristic (ROC) analysis. Results Based on the texture analysis with SVM model, the areas under the ROC curve (AUCs) were 0.890 for subtraction images, 0.736 for postcontrast images, and 0.672 for precontrast images, respectively. For LRA model, the AUCs were 0.884, 0.733, and 0.623, respectively. For QDA model, the AUCs were 0.831, 0.726, and 0.568, respectively. LRA and the SVM model with subtraction images reached significantly better performance than the QDA model (P= 0.0227 andP= 0.0088, respectively). Conclusion Texture features of breast cancer extracted from DCE-MRI are associated with HER2 2+ status. 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