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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?
Dandan Zhang1, Xiaochuan Geng2, Shiteng Suo3
1Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No.270 Dongan Rd., Shanghai 200032, People's Republic of China.
Purpose:
To explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer.
Methods:
Eighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR).
Results:
Correlations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and - 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC(0, 1000), MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively.
Conclusion:
MK was correlated with HR expression. ADC(0, 1000) and DKI were correlated with Ki67 expression. ADC(0, 1000) and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.
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