Advancing Neoadjuvant Chemotherapy Response Prediction through Deep Learning-Enabled Retrospective Quanfication of Pharmacokinetics

Motivation: While multiphasic contrast-enhanced MRI has propelled noninvasive pCR prediction in breast cancer, its limited temporal resolution restricts quantitative analysis, affecting generalizability and interpretability. Goal(s): To enhance pCR prediction, we integrated retrospective pharmacokinetic quantification by addressing the temporal resolution limit using deep learning. Approach: We incorporated a novel retrospective pharmacokinetic quantification approach into our pCR prediction model to better capture the tumor microenvironment's pharmacokinetic indicators. Results: Our approach improved predictive accuracy in external test datasets, demonstrating the method's superior performance and broader applicability. Impact: Deep-learning pharmacokinetic quantification enhances the accuracy and applicability of pCR prediction using multiphasic DCE-MRI, offering the potential for precise pre-treatment evaluation that could streamline NAC targeting and minimize initiation delays for breast cancer patients unlikely to respond to standard treatments.