Motivation: Early prediction of pathologic complete response (pCR) enhances personalized breast cancer treatment. Goal(s): This study aims to measure kinetic parameters from pre-neoadjuvant chemotherapy (NAC) ultrafast dynamic contrast-enhanced (DCE)-MRI using k-means clustering (KMC) to predict pCR. Approach: Fifty-six patients undergoing NAC were enrolled. Tumor and normal parenchymal voxels were divided into five clusters based on maximum enhancement rate. Ipsilateral/contralateral (I/C) background parenchymal enhancement kinetics (kBPE) parameters were compared between pCR and non-pCR patients. A logistic regression model incorporating clinical features. Results: MRI tumor kinetics, and kBPE I/Cs achieved an AUC of 0.94, with 0.91 sensitivity and specificity, for predicting breast cancer response to NAC. Impact: K-means clustering analysis of ultrafast DCE-MRI is a stable technique to effectively predict treatment response in breast cancer patients prior to neoadjuvant chemotherapy, which facilitates personalized therapy adjustments and can improve clinical outcomes through individualized treatments.
Ren et al. (Tue,) studied this question.