This study aimed to develop and validate a predictive model for the assessment of clinically significant prostate cancer (csPCa) in men, prior to prostate biopsies, based on bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters.
Materials and MethodsWe retrospectively analyzed 300 men with clinical suspicion of prostate cancer (prostatespecific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination [DRE]), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies (bpMRI-US transperineal FTSB) in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems (PI-RADS) scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [GS ≥ 3 + 4]) and compared by analyzing the areas under the curves and decision curves.
ResultsA predictive model defined by the combination of bpMRI and clinical parameters (age, PSA density) showed high discriminatory power (area under the curve, 0.861) and resulted in a significant net benefit on decision curve analysis. Applying a probability threshold of 7.5%, 21.6% of men could avoid unnecessary prostate biopsy, while only 1.0% of significant prostate cancers were missed.
ConclusionThis predictive model provided a reliable and measurable means of risk stratification of csPCa, with high discriminatory power and great net benefit. It could be a useful tool for clinical decision-making prior to prostate biopsies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.