Additional Value of Diffusion-Weighted Imaging to Evaluate Prognostic Factors of Breast Cancer: Correlation with the Apparent Diffusion Coefficient

Abstract

Background: Breast cancer is a heterogeneous disease with diverse prognoses The main prognostic determinants are lymph node status, tumor size, histological grade, and biological factors, such as hormone receptors, human epidermal growth factor receptor 2 (HER2), Ki-67 protein levels, and p53 expression. Diffusion-weighted imaging (DWI) can be used to measure the apparent diffusion coefficient(ADC) that provides information related to tumor cellularity and the integrity of the cell membranes. Objectives: The goal of this study was to evaluate whether ADC measurements could provide information on the prognostic factors of breast cancer. Patients and Methods: A total of 71 women with invasive breast cancer, treated consecutively who underwent preoperative breast MRI5 with DWI at 3.0 Tesla and subsequent surgery, were prospectively included in this study Each DWI was acquired with b values of 0 and 1000 simm2. The mean ADC values of the lesions were measured, including the entire lesion on the three largest sections. We performed histopathological analyses for the tumor size, lymph node status, histological grade, hormone receptors, human epidermal growth factor receptor 2 (HER2), Ki-67, p53, and molecular subtypes. The associations with the ADC values and prognostic factors of breast cancer were evaluated using the independent-samples t test and the one-way analysis of variance (ANOVA). Results: A loss ADC value was associated with lymph node metastasis (P< 0.01) and with high Ki-67 protein levels (P= 0.03). There were no significant differences in the ADC values among the histological grade (P = 0.48), molecular subtype (P = o.51), tumor size (P = 0.46), and p53 protein level (P= 0.62). Conclusion: The pre-operative use of the 3.0 Tesla DWI could provide information about the lymph node status and tumor proliferation for breast cancer patients, and could help determine the optimal treatment plan.