Prediction of Neoadjuvant Chemotherapy Response of Breast Cancer with Changes of MR Perfusion and Diffusion Characteristics in Early Chemotherapy by Using Neural Network Algorithm

Huiyan Shao¹, Li Guo², Xiaoying Wang², Rui Li¹, Chun Yuan, ¹³, Huijun Chen¹

Neoadjuvant Chemotherapy (NAC), locally enforced before the surgery, plays a significant role in the multimodality therapy of breast cancer. To predict whether a patient will have a complete response after the early NAC in-vivo is critical for the therapy plan. Diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have been reported great potential to predict NAC responder. However, most studies used the diffusion or perfusion imaging alone for prediction. In this study, we employed neural network and logistic regression to predict the final treatment response by using the changes of DW-MRI and DCE-MRI parameters after the early NAC cycles.