What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-Based Pharmacokinetic Quantification of DCE-MRI

Puntos clave

The model achieved superior pCR prediction performance across multiple datasets, indicating improved accuracy.
Pharmacokinetic parameters from DCE-MRI were used to enhance the prediction of treatment response.
The approach provides a noninvasive quantitative method for predicting chemotherapy response in clinical settings.
Generalizable results across datasets suggest broader applicability without changing imaging protocols.

Resumen

Motivation: Accurate prediction of pCR to neoadjuvant chemotherapy in breast cancer is crucial for personalized treatment. However, the generalizability of prediction algorithms from DCE-MRI is often hindered by imaging protocol discrepancy. Goal(s): This study aims to enhance the generalizability of pCR prediction through deep learning-based pharmacokinetic mapping. Approach: Using a previously developed DL model, pharmacokinetic parameters were retrospectively estimated from clinical multi-phasic DCE-MRI data across four public datasets, which were subsequently applied in radiomic analysis to predict pCR. Results: Our model demonstrated superior and consistent pCR prediction performance across datasets compared to conventional functional tumor volume (FTV) enhancement maps. Impact: This study introduces a quantitative, generalizable approach to early prediction of neoadjuvant chemotherapy (NAC) response in breast cancer, unlocking noninvasive imaging biomarkers with enhanced predictive accuracy and generalizability, thereby facilitating personalized treatment decisions without modifying clinical imaging protocols.

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