Article Abstract

Relationship between histogram metrics of pharmacokinetic parameters of DCE-MRI and histological phenotype in breast cancer

Authors: Guocai Yang, Jing Yang, Hui Xu, Qingxin Zhang, Yonghong Qi, Aiju Zhang


Background: To investigate the correlation between quantitative pharmacokinetic parameters and clinicopathological prognostic biomarkers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and MiB1 (Ki-67) in patients with breast cancer, the image histogram analysis was performed on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
Methods: The reference region model (RRM) was used to calculate the quantitative permeability parameters, including reference region volume transfer constant (Ktrans,RR), the rate constant of tissue of interest (Kep,TOI), and the rate constant of reference region (Kep,RR). Histogram analysis was performed to compare these parameters between ER/PR/HER2/Ki-67 positive and negative groups. The performance of the histogram parameters Ktrans,RR, Kep,TOI and Kep,RR in differential diagnosis of immunohistochemistry results was conducted by receiver operating characteristic (ROC) curve analysis.
Results: All the histogram metrics of Kep,TOI significantly differed between ER/PR positive and negative groups (P<0.05). However, Ktrans,RR and Kep,RR did not significantly differ between ER/PR positive and negative groups (P>0.05). The 10th percentile, energy, entropy and variance of Ktrans,RR, and almost all the histogram parameters of Kep,TOI except for variance significantly differed between HER2 positive and negative groups (all P<0.05). The energy of Ktrans,RR significantly differed between Ki-67 positive and negative groups (P<0.05). The skewness and energy of Kep,TOI showed the highest AUC of 0.977 and 0.879 in differentiating ER/PR status. The energy of Ktrans,RR presented the highest AUC in the differentiation of HER2 and Ki-67.
Conclusions: Histogram analysis on quantitative pharmacokinetic breast parameters using DCE-MRI improves the performance in differentiation of histological phenotypes of breast cancer.