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$\sim$0.86) was superior to (p$<$.05) the conventional model (AUCs=0.68$\sim$0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80$\sim$0.90, $p<.05$). For task 2, the combined MBV-DWI model (AUCs=0.85$\sim$0.89) performed better than ($p<.05$) the conventional MBV-DWI model (AUCs=0.73$\sim$0.82). In addition, for the combined DCE-MRI model and the combined MBV-DWI model, the former (AUCs=0.85$\sim$0.90) outperformed ($p<.05$) the latter (AUCs=0.80$\sim$0.85) in task 1, whereas the latter (AUCs=0.85$\sim$0.89) outperformed ($p<.05$) the former (AUCs=0.76$\sim$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.