Multi-parametric MRI Radiomics Predicts Different HER2 Expression in Breast Cancer

Purpose To develop and validate radiomic models using multi-parametric dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent movement (IVIM)-based features for the preoperative differentiation of HER2 expressions levels in breast cancer. Materials and Methods This retrospectively study analyzed 227 female breast cancer patients who underwent breast 3.0TMRI examination at our institution from December 2019 to December 2023. The least absolute shrinkage and selection operator (LASSO) ten-fold cross-validation method was used to develop the radiomic features to identify HER2 positive and HER2 negative cancer(task 1), and further identify HER2 low and HER2 zero cancer(task 2). Then the radiomic features were selected and combined with clinical characteristics to construct predicting models using the logistic regression analysis. The area under the receiver operating characteristic curve(AUC), sensitivity, and specificity were used to evaluate the performance of radiomic models. Results For task 1, the AUCs of clinical model (histological grade and peritumoral edema), DCE combined IVIM(D + D*+f) radiomic model and clinic combined radiomic model were 0.785 (95%CI:0.713,0.846), 0.866 (95%CI:0.803,0.915) and 0.903 (95%CI:0.846,0.944) respectively. In the validation cohort, The AUCs were 0.751 (95%CI:0.633,0.848), 0.751 (95%CI:0.633,0.848) and 0.830 (95%CI:0.720,0.910) respectively. For task 2, the AUCs of DCE combined IVIM radiomic model in training and validation cohort were 0.951 (95%CI:0.888,0.984) and 0.853 (95%CI:0.712,0.942) respectively, and the radiomics score was independent predictors of HER2 low cancer. Conclusion The radiomic signature derived from multi-parametric MRI, together with peritumoral edema and histological grade, demonstrated strong performance in predicting HER2 expression preoperatively in breast cancer, which may support individualized treatment strategies.