Prostate 

Biparametric Prostate MRI and Clinical Indicators Predict Clinically Significant Prostate Cancer in Men With "Gray Zone" PSA Levels - PDF Version

Chao-Gang Wei , Tong Chen  , Yue-Yue Zhang , Peng Pan, Guang-Cheng Dai, Hong-Chang Yu, Shuo Yang, Zhen Jiang, Jian Tu, Zhi-Hua Lu, Jun-Kang Shen, Wen-Lu Zhao 

Eur J Radiol  2020 Apr 10;127:108977.  
doi: 10.1016/j.ejrad.2020.108977. Online ahead of print.   

PURPOSE:  

To predict clinically significant prostate cancer (cs-PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators in men with prostate-specific antigen (PSA) levels in the grey zone of 4-10 ng/ml.  

METHOD:  

We retrospectively analysed 364 patients with elevated PSA levels in the grey zone who had pathologically confirmed disease and had undergone MRI examinations from January 2015 to October 2019; a training group (n = 255) and validation group (n = 109) were randomly established. Multivariate logistic regression analysis of the training group was performed to identify the independent predictors for cs-PCa, thereby establishing a predictive model that was evaluated in the training and validation groups by analysing the receiver operating characteristic (ROC) curve.  

RESULTS:  

In the training group, the PI-RADS v2 score and prostate volume (PV) were independent predictors of cs-PCa (P < 0.05). The prediction model comprising the PI-RADS v2 score and PV had a larger area under the curve than the other predictors alone in the training group. The diagnostic sensitivity and specificity of the prediction model were 84.1 % and 83.4 %, respectively. The prediction model was indicated to have better predictive performance in the validation group.  

CONCLUSIONS:  

The prediction model exhibits a satisfactory predictive value for cs-PCa in men with PSA levels in the grey zone. PI-RADS v2 is the strongest univariate predictor for the detection of cs-PCa in men with PSA in the grey zone, but combining this with the PV can provide superior predictive ability.