ArticleCancer research and treatment2021
A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population.
Article in Cancer research and treatment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis.JAMA network open · 2024Pooled it
- Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review.Cancers · 2022Review
- Efficacy of Tadalafil in Penile Rehabilitation Started Before Nerve-Sparing Robot-Assisted Radical Prostatectomy: A Double-Blind Pilot Study.Sexual medicine · 2022Article
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Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeThis 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 (prostate-specific antigen [PSA] ≥ 4.0 ng/mL and/or abnormal findings in a digital rectal examination), who underwent bpMRI-ultrasound fusion transperineal targeted and systematic biopsies in the same session, at a Korean university hospital. Predictive models, based on Prostate Imaging Reporting and Data Systems scores of bpMRI and clinical parameters, were developed to detect csPCa (intermediate/high grade [Gleason score ≥ 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.
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