Keywords: Breast, Cancer, DCE Model
Motivation: Using super voxels (SVs) to average DCE-MRI time courses from localized regions before pharmacokinetic (PK) modeling may reduce the effects from noise and motion while still capturing tumor heterogeneity.
Goal(s): Evaluate how the use of SVs affects the predictive performance of Ktrans for breast cancer (BC) response to neoadjuvant chemotherapy (NAC).
Approach: 26 BC patients treated with NAC underwent longitudinal DCE-MRI, which were fitted with the Tofts model and Shutter-Speed model (SSM) using a voxel-wise and SV-based approach.
Results: Combining the SV approach with SSM can substantially improve Ktrans predictive performance.
Impact: Use of SVs to stabilize advanced DCE-MRI PK models may potentially improve accuracy of estimated Ktrans and its predictive performance for BC response to NAC.
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