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Background Half of breast cancers exhibit low expression levels of human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry IHC score of 0), HER2-low (IHC score of 1+ or 2+ with negative findings at fluorescence in situ hybridization FISH), and HER2-positive (IHC score of 2+ with positive findings at FISH or IHC score of 3+) breast cancers were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features can help distinguish HER2 expressions in breast cancer. Materials and Methods This study included women with breast cancer who underwent MRI at two different centers between December 2020 and December 2022. Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used for the selection of features to build a radiomics signature. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the radiomic signature. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions in both the training and prospectively acquired external data set. Results The training set included 208 patients from center 1 (mean age, 53 years ± 14 SD), and the external test set included 131 patients from center 2 (mean age, 54 years ± 13). In the external test data set, the radiomic signature achieved an AUC of 0.80 (95% CI: 0.71, 0.89) for distinguishing HER2-low and -positive tumors versus HER2-zero tumors and was a significant predictive factor for distinguishing these two groups (odds ratio = 7.6; 95% CI: 2.9, 19.8; P Supplemental material is available for this article. See also the editorial by Kataoka and Honda in this issue.
Ramtohul et al. (Tue,) studied this question.