Synopsis

Keywords: Prostate, Prostate

The aim of this study was to investigate the use of advanced diffusion-weighted imaging to sub-differentiation of intermediate risk group PCa and aid treatment decision process. Finally, the logistic model was statistically significant, χ2=90.969, p<0.001. The model could correctly classify 86.40% of the subjects. The results of ROC analysis showed that area AUC was 0.9429, CI 95%: 0.9054-0.9805. Sensitivity, specificity, positive predictive value and negative predictive value were 88.06%, 84.31%, 88.06% and 84.31%, respectively. The results of this research may indicate that advanced diffusion-weighted imaging can sub-differentiate intermediate risk group PCa and aid the decision process of treatment.

Introduction

Prostate cancer (PCa) is the most common non-cutaneous cancer in man, the incidence rate of PCa increased with age, about 85% of PCa patients are older than 65, and less than 1% are younger than 50(1). In order to select the appropriate treatment options and predict the prognosis of PCa patients, the NCCN Guidelines of PCa have incorporated a risk stratification scheme that uses a minimum of stage, Gleason grade, and prostate specific antigen (PSA) to assign patients to risk groups, including very low-, low-, favorable intermediate-, unfavorable intermediate-, high- and very high-risk group(2). For intermediate risk group PCa, some studies have showed that unfavorable intermediate-risk group (unFIRG)PCa patients had worse PSA recurrence-free survival and higher rates of distant metastasis and PCa-specific mortality than the favorable intermediate-risk group (FIRG) PCa patients(3). In recent years, some studies have demonstrated that diffusion weighted imaging (DWI) or advanced DWI could differentiate Gleason pattern ≤ 3 + 4 PCa and Gleason pattern ≥ 3 + 4 PCa. To our knowledge, the role of advanced DWI in the sub-differentiation of intermediate risk group PCa has not been reported. Therefore, the purpose of this study is sub-differentiation of intermediate risk group PCa using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the treatment decision process.

Methods

Our study was approved by the institutional review board and all patients signed informed consent at screening. 372 patients underwent prostate MRI examination at 3.0 T MRI (GE Discovery MR 750W) with 8-channel phased array coil, including conventional scanning sequences, IVIM, stretched exponential model and DKI. Finally, 118 intermediate-risk group PCa patients were included in the present study, including 51 FIRG PCa patients and 67 unFIRG PCa patients. Acquisition of quantitative data was carried on GE AW 4.6 workstation. ROI was then drawn on the parameter maps of IVIM, stretched exponential model and DKI. Three ROIs with area of 50 mm2 were randomly drawn at the maximum slicer of the lesion, and the average value was recorded. In order to examine the association between parameters of DKI, IVIM, and stretched exponential model with the probability of unFIRG PCa in intermediate risk group PCa, binary logistic regression analysis was applied. After binary logistic regression analysis, the receiver operating characteristic (ROC) curve of combination parameters, which were statistically significant in binary logistic regression analysis, were analyzed to test the diagnostic efficiency of detecting unFIRG PCa in intermediate risk group PCa.

Results

A total of 118 intermediate-risk group PCa patients were included, including 51 patients were FIRG (Figure 1), and 67 patients were unFIRG (Figure 2). Finally, the logistic model was statistically significant, χ2=90.969, p<0.001. The model could correctly classify 86.40% of the subjects. FA (p<0.001), MD (p=0.031), MK (p=0.014), D (p=0.041), DDC (p=0.001) were statistically significant. Odds ratio (CI 95%) were 6.856 (2.679-17.545), 3.597 (1.292-10.013), 3.018 (1.107-8.227), 0.411 (0.175-0.962), 0.177 (0.062-0.510), respectively (Figure 3). The results of ROC analysis showed that area under the curve (AUC) was 0.9429, CI 95%: 0.9054-0.9805. Sensitivity, specificity, positive predictive value and negative predictive value were 88.06%, 84.31%, 88.06% and 84.31%, respectively.

Discussion

This study used advanced DWI (IVIM, stretched exponential model and DKI) to differentiate FIRG group PCa and unFIRG PCa in intermediate risk group PCa. The results showed that combination of parameter D of IVIM, parameter DDC of stretched exponential model and parameters FA, MD and MK of DKI have ability to differentiate FIRG group PCa and unFIRG PCa in intermediate risk group PCa and have high diagnostic efficacy for unFIRG group PCa. This may be attributed to the difference in histological changes of FIRG group PCa and unFIRG group PCa, including vascular (i.e., capillaries), fibromuscular stroma, epithelium, and glandular lumen. Compared with FIRG group PCa, unFIRG group PCa contains increased volumes of low- restriction diffusivity epithelial cells and decreased high-restriction diffusivity stroma and lumen space. In this study, parameters pseudodiffusion coefficient (D*) and perfusion fraction (f) of IVIM, parameter heterogeneity index (α) of stretched exponential model also have no statistical significance. A reasonable explanation may be that the perfusion difference between FIRG group PCa and unFIRG group PCa may not be enough to be detected by D* and f in TZ.

Acknowledgements

No acknowledgement found.