LG188: Non-Gaussian water diffusion kurtosis imaging of prostate cancer

Date: 
Friday, May 16, 2014 - 13:00
Speaker: 
Shoshana Ginsburg
Abstract: 
Purpose To evaluate the non-Gaussian water diffusion properties of prostate cancer (PCa) and determine the diagnostic performance of diffusion kurtosis (DK) imaging for distinguishing PCa from benign tissues within the peripheral zone (PZ), and assessing tumor lesions with different Gleason scores. Materials and Methods Nineteen patients who underwent diffusion weighted (DW) magnetic resonance imaging using multiple b-values and were pathologically confirmed with PCa were enrolled in this study. Apparent diffusion coefficient (ADC) was derived using a monoexponential model, while diffusion coefficient (D) and kurtosis (K) were determined using a DK model. Differences between the ADC, D and K values of benign PZ and PCa, as well as those of tumor lesions with Gleason scores of 6, 7 and ≥ 8 were assessed. Correlations between parameters D and K in PCa were analyzed using Pearson’s correlation coefficient. ADC, D and K values were correlated with Gleason scores of 6, 7 and ≥ 8, respectively. Results ADC and D values were significantly (p < 0.001) lower in PCa (0.79 ± 0.14 μm2/ms and 1.56 ± 0.23 μm2/ms, respectively) compared to benign PZ (1.23 ± 0.19 μm2/ms and 2.54 ± 0.24 μm2/ms, respectively). K values were significantly (p < 0.001) greater in PCa (0.96 ± 0.20) compared to benign PZ (0.59 ± 0.08). D and K showed fewer overlapping values between benign PZ and PCa compared to ADC. There was a strong negative correlation between D and K values in PCa (Pearson correlation coefficient r = − 0.729; p < 0.001). ADC and K values differed significantly in tumor lesions with Gleason scores of 6, 7 and ≥ 8 (p < 0.001 and p = 0.001, respectively), although no significant difference was detected for D values (p = 0.325). Significant correlations were found between the ADC value and Gleason score (r = − 0.828; p < 0.001), as well as the K value and Gleason score (r = 0.729; p < 0.001). Conclusion DK model may add value in PCa detection and diagnosis. K potentially offers a new metric for assessment of PCa.