In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65-5.86] vs. 3.54 [2.49-4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05-35.95] vs. 27.90 [21.43-33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80-5.88] vs. 4.52 [3.80-5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs. 0.819, p < 0.001) and csPCa (0.827 vs. 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.
Keywords: Adipose tissue; biopsy; magnetic resonance imaging; nomogram; prostate cancer.