• Yu Zhang, Na Zeng, FengBo Zhang, YangXinRui Huang, and Ye Tian.
• Department of Urology, Beijing Friendship Hospital, Capital Medical University, No. 95, Yongan Road, Xicheng District, Beijing, People's Republic of China.
• Int. J. Clin. Oncol. 2020 Jan 1; 25 (1): 175-186.

PurposeTo determine whether patients can avoid systematic prostate biopsy (PBx) if their Prostate Imaging Reporting and Data System version 2 (PI-RADs v2) score is ≤ 3 and how we clinicians make decisions that can maximize benefit.Materials And MethodsWe reviewed our prospectively maintained database of consecutive men who received transrectal ultrasound-guided 24-core biopsy as well as pre-biopsy multi-parametric magnetic resonance imaging (mp-MRI). Of the 1276 men who were performed PBx in our institution from 2012 to July 2018, 491 patients conformed to the criteria. Negative predictive value (NPV) of negative mp-MRI (defined as PI-RADs < 3) combined prostate-specific antigen density (PSAD) were calculated. Models based on PI-RADs v2 were developed to predict the absence of clinically significant prostate cancer (CSPCa) and prostate cancer (PCa). Nomograms as well as receiver operating curves (ROC) were established to estimate the discrimination. Calibration curves were used to assess the concordance between predictive value and true risk. Decision curves were made to measure the overall net benefit.ResultsProstate cancer and CSPCa detection rates were 21.6%, 7.3% and 36.7%, 23.4% in PIRADs v2 < 3 cohort and PIRADs v2 = 3 cohort, respectively. Men with biopsy-proved CSPCa had higher prostate-specific antigen (PSA), lower prostate volume (PV) and higher PSAD (all p < 0.05 in the two cohorts) than patients with clinically insignificant prostate cancer (CIPCa) or negative results. NPV of negative mp-MRI for detection of PCa was much higher when the PSAD was less than 0.15 (p < 0.001) and 0.2 for CSPCa (p = 0.007). According to multivariate analysis, we developed the model comprising Age, PSAD and PI-RADs v2 to predict the absence of CSPCa and PCa. The area under the curve (AUC) of the model for non-CSPCa was 0.75 (95% CI 0.68-0.80, PSAD cutoff 0.20), better than 0.71 (95% CI 0.65-0.80, PSAD cutoff 0.15). As for model for non-PCa, the AUC was 0.76 (95% CI 0.70-0.80, PSAD cutoff 0.15), higher than 0.71(95% CI 0.67-0.78, PSAD cutoff 0.20). Internally validated calibration curves showed that the model might overestimated the risk of the absence of CSPCa when the threshold was between 53 and 72%, and if the threshold was between 72 and 87%, it might underestimate the risk. As for the absence of PCa, the model might overestimate the risk between 52 and 76%. Decision curves showed that a better clinical net benefit was met when the threshold was 55% for non-PCa and 70% for non-CSPCa.ConclusionsNPV of negative mp-MRI for detection of CSPCa and PCa was improved with decreasing PSAD. The nomograms based on PI-RADs v2, age and PSAD showed internally validated high discrimination and calibration for the absence of PCa and CSPCa. When the predictive value was greater than 70% for the absence of CSPCa and 55% for the absence of PCa, we could avoid unnecessary PBx to maximize net benefit.

25 keywords...

hide…