Objective The initial MRI and clinicopathological data of 299 patients with breast cancer confirmed by pa-thology in our hospital were retrospectively collected.The patients were divided into low-HER-2 expression group(n=144)and non-low-HER-2 expression group(n=155).They were randomly divided into training set(n=239)and testing set(n=60)according to the ratio of 8∶2.ITK-SNAP software was used to manually delineate the region of interest(ROI)of the lesions on DCE-MRI and DWI images,which were used to extract radiomics features.The methods of Mann-Whitney U test,Z-score normalization,variance threshold,K-best,least absolute shrinkage and selection operator(LASSO)were used to se-lect radiomics features.The models of DCE-MRI,DWI and DCE-MRI combined DWI were established.The area under the curve(AUC)of receiver operating characteristic(ROC),sensitivity,specificity and accuracy were used to evaluate the pre-dictive performance of the models.Results The AUC values of predictive models of HER-2 low expression based on DCE-MRI,DWI,and the combined models in the trainning and testing sets were 0.754,0.775,0.843 and 0.774,0.645,0.795,respectively.Conclusion Both radiomics feature models based on DCE-MRI and DWI could preoperative predict HER-2 low expression status noninvasively in breast cancer,especially for the combined model,which could be helpful for the selec-tion of clinical treatment in breast cancer.
Breast cancerHuman epidermal growth factor receptor-2Magnetic resonance imagingDiffusion weigh-ted imagingRadiomics
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