A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Populationopen access
- Authors
- Noh, Tae Il; Hyun, Chang Wa; Kang, Ha Eun; Jin, Hyun Jung; Tae, Jong Hyun; Shim, Ji Sung; Kang, Sung Gu; Sung, Deuk Jae; Cheon, Jun; Lee, Jeong Gu; Kang, Seok Ho
- Issue Date
- Oct-2021
- Publisher
- KOREAN CANCER ASSOCIATION
- Keywords
- Prostatic neoplasms; Bi-parametric magnetic resonance imaging; Transperineal prostate biopsy; Nomograms
- Citation
- CANCER RESEARCH AND TREATMENT, v.53, no.4, pp 1148 - 1155
- Pages
- 8
- Journal Title
- CANCER RESEARCH AND TREATMENT
- Volume
- 53
- Number
- 4
- Start Page
- 1148
- End Page
- 1155
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72334
- DOI
- 10.4143/crt.2020.1068
- ISSN
- 1598-2998
2005-9256
- 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), 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. 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.
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