Keywords: Breast, Breast
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.
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