Abstract
Selective identification of men with clinically significant prostate cancer (sPC) is a pivotal issue. Development of a risk model for detecting sPC based on the prostate imaging reporting and data system (PI-RADS) for bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters in a Japanese cohort is expected to prove beneficial. We retrospectively analyzed clinical parameters and bpMRI findings from 773 biopsy-naïve patients between January 2011 and December 2016. A risk model was established using multivariate logistic regression analysis and presented on a nomogram. Discrimination of the risk model was compared using the area under the receiver operating characteristic curve. Statistical differences between the predictive model and clinical parameters were analyzed using DeLong test. sPC was detected in 343 men (44.3%). Multivariate logistic regression analysis to predict sPC revealed age (P = 0.002), log prostate-specific antigen (P < 0.001), prostate volume (P < 0.001) and PI-RADS scores (P < 0.001) as significant contributors to the model. Area under the curve was higher for the risk model (0.862), than for age (0.646), log prostate-specific antigen (0.652), prostate volume (0.697) or imaging score (0.822). DeLong test results also showed that the novel risk model performed significantly better than those parameters (P < 0.05). This novel risk model performed significantly better compared with PI-RADS scores and other parameters alone, and is thus expected to prove beneficial in making decisions regarding biopsy on suspicion of sPC.
Highlights
Selective identification of men with clinically significant prostate cancer is a pivotal issue
DeLong test results showed that the novel risk model performed significantly better compared with those parameters including prostate imaging reporting and data system (PI-RADS) score alone (Table 3)
While multi-parametric magnetic resonance imaging (mpMRI) can detect 85–95% of significant prostate cancer (sPC) compared with prostatectomy specimens, the sensitivity, negative predictive value (NPV) and specificity of mpMRI have been reported as 58–96%, 63–98% and 23–87%, respectively[5,21]
Summary
Selective identification of men with clinically significant prostate cancer (sPC) is a pivotal issue. Development of a risk model for detecting sPC based on the prostate imaging reporting and data system (PI-RADS) for bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters in a Japanese cohort is expected to prove beneficial. Multivariate logistic regression analysis to predict sPC revealed age (P = 0.002), log prostate-specific antigen (P < 0.001), prostate volume (P < 0.001) and PI-RADS scores (P < 0.001) as significant contributors to the model. Population-based prostatespecific antigen (PSA) screening tests can facilitate early detection of prostate cancer and lead to declines in prostate cancer related-mortality[2] These tests simultaneously lack specificity, resulting in increased numbers of unnecessary prostate biopsies, which in turn are associated with risks of rectal bleeding and sepsis. Previous multivariable prediction models for detecting sPC were based on clinical parameters including various combinations of age, PSA, prostate volume (PV), DRE findings and others. Predictive models based on bpMRI findings and clinical parameters for risk assessment and selection of sPC have recently been r eported[14,15]
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