Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
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.
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