1. A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population
- Author
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Deuk Jae Sung, Seok Ho Kang, Sung Gu Kang, Jeong Gu Lee, Ji Sung Shim, Jun Cheon, Chang Wan Hyun, Tae Il Noh, Hyunjung Jin, Jong Hyun Tae, and Ha Eun Kang
- Subjects
Image-Guided Biopsy ,Male ,Cancer Research ,medicine.medical_specialty ,Prostate biopsy ,Clinical Decision-Making ,Genitourinary Cancer ,Prostate cancer ,Prostate ,Republic of Korea ,medicine ,Transperineal prostate biopsy ,Biomarkers, Tumor ,Humans ,Parametric statistics ,Aged ,Retrospective Studies ,Ultrasonography ,medicine.diagnostic_test ,business.industry ,Patient Selection ,Area under the curve ,Prostatic Neoplasms ,Magnetic resonance imaging ,Rectal examination ,Nomogram ,Bi-parametric magnetic resonance imaging ,medicine.disease ,Prognosis ,Magnetic Resonance Imaging ,Nomograms ,medicine.anatomical_structure ,Oncology ,Original Article ,Radiology ,business ,Follow-Up Studies - Abstract
Purpose 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 methods We 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 [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. Results A 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. Conclusion This 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.
- Published
- 2020