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Cancer Research and Treatment > Accepted Articles
doi: https://doi.org/10.4143/crt.2020.1068    [Accepted]
A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population
Tae Il Noh1, Chang Wan Hyun1, Ha Eun Kang1, Hyun Jung Jin1, Jong Hyun Tae1, Ji Sung Shim1, Sung Gu Kang1, Deuk Jae Sung1,2, Jun Cheon1, Jeong Gu Lee1, Seok Ho Kang1
1Department of Urology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
2Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
Correspondence  Seok Ho Kang ,Tel: 82-2-920-5530, Fax: 82-2-928-7864, Email: mdksh@korea.ac.kr
Received: October 18, 2020;  Accepted: December 31, 2020.  Published online: December 31, 2020.
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.
Key words: Prostatic neoplasms, Bi-parametric MRI, Transperineal prostate biopsy, Nomograms
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