MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy

Purpose: To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2). Materials and Methods : Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion- weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional radiomic features were extracted from both DCE-MRI and MBV-DWI. After feature selection, the following models were built using the retained features and logistic regression: vector model, conventional model, and combined model that integrates the vector-radiomic and conventional-radiomic features. The models' performances were quantified by the area under the receiver-operating characteristic curve (AUC). Results: The training/external test set (center 1/2) included 483/361 women. For task 1, the vector model (AUCs=0.73 similar to 0.86) was superior to (p<.05) the conventional model (AUCs=0.68 similar to 0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80 similar to 0.90, p < .05). For task 2, the combined MBV-DWI model (AUCs=0.85 similar to 0.89) performed better than (p < .05) the conventional MBV-DWI model (AUCs=0.73 similar to 0.82). In addition, for the combined DCEMRI model and the combined MBV-DWI model, the former (AUCs=0.85 similar to 0.90) outperformed (p < .05) the latter (AUCs=0.80 similar to 0.85) in task 1, whereas the latter (AUCs=0.85 similar to 0.89) outperformed (p < .05) the former (AUCs=0.76 similar to 0.81) in task 2. The above results are true for the training and external test sets. Conclusions: MRI-based vector radiomics may predict BC HER2 status and its changes after NAT and provide significant incremental prediction over and above conventional radiomics.