K-means clustering-based analysis of quantitative ultrafast DCE-MRI for predicting breast cancer response to...

Achieving a pathologic complete response (pCR) following neoadjuvant chemotherapy (NAC) is strongly associated with improved survival. This study investigates whether bilateral asymmetry of quantitative perfusion parameters in normal parenchyma from ultrafast dynamic contrast-enhanced MRI (DCE-MRI), measured using k-means clustering (KMC) before NAC, can predict pCR in breast cancer patients.

Fifty-six breast cancer patients undergoing NAC with pretreatment ultrafast DCE-MRI (3-9 s/image at 3T) were enrolled. KMC was used to classify tumor and normal parenchymal voxels into five clusters based on maximum enhancement rate (A·α). Ipsilateral-to-contralateral (I/C) ratios of background parenchymal enhancement kinetics (kBPE) and tumor kinetics (kT) were compared between pCR and nonpCR groups. Logistic regression models were developed to predict pCR. Statistical tests included bootstrapping, z-test, chi-square, and Wilcoxon rank-sum.

Patients with residual disease showed significantly higher kBPE in the normal-appearing parenchyma of the ipsilateral breast compared to the contralateral side. Parameters including enhancement rate α, A·α, area under the enhancement curve for 30 s AUC30, volume transfer constant K^{tran s}, and rate constant of contrast transfer, K_ep, were significantly higher, while extravascular extracellular space fractional volume, v_e, was significantly lower in the ipsilateral breast parenchyma versus contralateral breast parenchyma for women who have residual disease (p < 0.05). A prediction model using kBPE asymmetry alone achieved an area under the curve (AUC) of 0.83. Including tumor kinetics improved the AUC to 0.85.

Bilateral asymmetry of kBPE parameters derived from ultrafast DCE-MRI using KMC before NAC initiation can predict pCR with high accuracy, providing a new minimal-invasive biomarker for treatment response.