Discussion of correlation between histogram analysis of quantitative diffusion weighted imaging and Gleason score of prostate cancer

Abstract

Objective: To discuss the correlation between histogram analysis of quantitative mono-exponential, bi-exponential and diffusion kurtosis models in diffusion weighted imaging and the Gleason score of prostate cancer, and evaluate the application value and diagnostic efficiency in identifying low and high grade prostate cancer. Methods: A total of 50 patients with histologically confirmed as prostate cancer were examined from May 2015 to May 2016 in the Second Affiliated Hospital of Soochow University using DWI performed at 3.0 T with an extended b-value range from 0 to 2 000 s/mm(2). Data were post-processed by whole tumor histogram analysis,the ROI was manually drown in DWI (b=1 000 s/mm(2)) step by step along the outline of cancer, and quantitative analysis were performed respectively by mono-exponential, bi-exponential and diffusion kurtosis models for quantification of apparent diffusion coefficients (ADCs), diffusivity D, pseudo-diffusivity D(*), perfusion fraction f, diffusion coefficients by non-Gaussian distribution (D(k)) and kurtosis coefficient (K).Then the histogram analysis was performed to get the mean, median, 25th percentile, 75th percentile, skewness and kurtosis. The correlation between histogram analysis results of these quantitative parameters and Gleason score of prostate cancer were evaluated by Spearman correlation coefficient. The diagnostic performance of histogram analysis results of each quantitative parameters in identifying low (Gleason score≤6) and high (Gleason score>6) grade prostate cancer was performed by comparing the area under the ROC curve and the curve values. Results: The values of ADC, D and D(k) (mean, median, 25th, 75th) were negatively correlated with Gleason score of prostate cancer (r value was -0.388--0.624, P<0.05). The values of D (skewness and kurtosis) had a certain correlation with Gleason score of prostate cancer (r value were 0.413 and 0.402, P<0.05). The histogram analysis results of D(*) and f had no statistically significant correlation with Gleason score of prostate cancer (P>0.05). The values of K (mean, median, 25th, kurtosis) were positively correlated with Gleason score of prostate cancer (r value was 0.423-0.699,P<0.05). The diagnostic efficiency of histogram analysis results of these quantitative parameter values in identifying low and high grade prostate cancer showed that the ADC (median), D (25th), D(k) (mean) and K (25th) had a larger area under the curve, and were 0.844, 0.873, 0.815, and 0.919 respectively, the differences of area under the curve between any two of these parameters above were not statistically significant (all P>0.05). Conclusions: The quantitative parameters of three diffusion models (ADC, D, D(k), K) in DWI are all related to the Gleason score of prostate cancer, but in the differential diagnosis of low and high grade prostate cancer, the diagnostic efficacy of mono-exponential model is sufficient. The more complex model such as bi-exponential and diffusion kurtosis may complement it in other ways.