Prediction of Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer Using a 3D Neural Network on Synthetic MR Images

Motivation: There is an unmet need for noninvasive imaging biomarkers for accurate early prediction of therapy response in patients with triple negative breast cancer. Goal(s): In this study, we investigated using a 3D convolutional neural network to predict response from quantitative T1, T2, and PD images acquired with the SyntheticMR technique. Approach: A ResNet-101 network was extended to 3D with 3 channels of inputs, and trained and evaluated using 5-fold cross validation with 217 image datasets acquired after 2 and 4 cycles of therapy. Results: Accuracy of response prediction ranged from 0.61-0.77 and dice similarity ranged from 0.68-0.77 when all time points were utilized. Impact: Therapy response at mid-treatment may be determined by a neural network applied to a single multi-parameter mapping sequence, which may help guide treatment strategies for improved outcomes for patients with triple negative breast cancer.