Differentiation of prostate cancer and benign prostatic hyperplasia: comparisons of the histogram analysis of intravoxel incoherent motion and monoexponential model with in-bore MR-guided biopsy as pathological reference.

Abstract

To evaluate the diagnostic performance of histogram analysis of intravoxel incoherent motion (IVIM) parameters for differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH), and compare with the monoexponential model, with in-bore MR-guided biopsy as pathological reference. Thirty patients were included in this study. DWI images were processed with Matlab R2015b software by IVIM and monoexponential model for quantitation of diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC). The multiparametric data were compared between PCa and BPH group. Correlations between parameters and Gleason scores of PCa were assessed with Spearman rank test. ROC analysis was used to evaluate and compare the diagnostic ability of each parameter for discriminating PCa from BPH. Logistic regression model was used to evaluate the diagnostic performance of combination of different histogram parameters. Sixteen PCa lesions and 20 BPH nodules were analyzed in this study. For IVIM-derived D, the histogram mean, 75th, 90th, and max of PCa were significantly lower than BPH. PCa had significantly lower min and 10th D* than BPH. For f, histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and skew showed significant differences between PCa and BPH. For ADC, PCa were significantly lower than BPH in terms of histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and kurtosis. Histogram mean D and min, 25th D* show significantly negative correlation with Gleason score (r = - 0.582, - 0.534, - 0.554, respectively). Histogram max D and mean f and min ADC showed higher diagnostic performance than other parameters (AUC = 0.925, 0.881, 0.969, respectively). The IVIM model (combined with max D, min D* and mean f) (AUC = 0.950 [0.821, 0.995]) didn't show significant difference from the monoexponential model (AUC = 0.969 [0.849, 0.999], p = 0.23). Besides, combination of the IVIM and monoexponential model didn't improve diagnostic performance compared with the single model (p = 0.362 and 0.763, respectively). Histogram analyses of IVIM and monoexponential model were both useful methods for discriminating PCa from BPH. The diagnostic performance of IVIM model seemed to be not superior to that of monoexponential model. Combination of IVIM and monoexponential model did not add significant information to the single model alone.