Preoperative Prediction of Her2-zero, -low and -overexpression Breast Cancers Using Multiparametric MRI and Machine Learning Modeling

Motivation: Her2-low breast cancers could benefit from new anti-HER2 therapies. Goal(s): To construct a preoperative prediction model of HER2 expression levels using multiparametric MRI and machine learning (ML) algorithms. Approach: 621 patients were investigated. Four ML methods were used to build models based on MRI features to predict HER2 expression levels. Results: MRI features of multiple lesions, spiculated margin, peritumoral edema and largest diameter were selected to build the models. ML models performed better for predicting HER2-zero vs. HER2-low/-overexpression than HER2-low vs. HER2-overexpression. The best model was KNN of AUC 0.86, sensitivity of 76%, specificity of 73%, and accuracy of 75%. Impact: MRI features of breast cancer are associated with different HER2 expression levels. MRI-based ML models have the potential to preoperatively predict the HER2 expression status.