A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population
DC Field | Value | Language |
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dc.contributor.author | Noh, Tae Il | - |
dc.contributor.author | Hyun, Chang Wa | - |
dc.contributor.author | Kang, Ha Eun | - |
dc.contributor.author | Jin, Hyun Jung | - |
dc.contributor.author | Tae, Jong Hyun | - |
dc.contributor.author | Shim, Ji Sung | - |
dc.contributor.author | Kang, Sung Gu | - |
dc.contributor.author | Sung, Deuk Jae | - |
dc.contributor.author | Cheon, Jun | - |
dc.contributor.author | Lee, Jeong Gu | - |
dc.contributor.author | Kang, Seok Ho | - |
dc.date.accessioned | 2024-02-23T03:30:17Z | - |
dc.date.available | 2024-02-23T03:30:17Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 1598-2998 | - |
dc.identifier.issn | 2005-9256 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72334 | - |
dc.description.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. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | KOREAN CANCER ASSOCIATION | - |
dc.title | A Predictive Model Based on Bi-parametric Magnetic Resonance Imaging and Clinical Parameters for Clinically Significant Prostate Cancer in the Korean Population | - |
dc.type | Article | - |
dc.identifier.doi | 10.4143/crt.2020.1068 | - |
dc.identifier.bibliographicCitation | CANCER RESEARCH AND TREATMENT, v.53, no.4, pp 1148 - 1155 | - |
dc.identifier.kciid | ART002764950 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000744519500005 | - |
dc.identifier.scopusid | 2-s2.0-85118296446 | - |
dc.citation.endPage | 1155 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1148 | - |
dc.citation.title | CANCER RESEARCH AND TREATMENT | - |
dc.citation.volume | 53 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Prostatic neoplasms | - |
dc.subject.keywordAuthor | Bi-parametric magnetic resonance imaging | - |
dc.subject.keywordAuthor | Transperineal prostate biopsy | - |
dc.subject.keywordAuthor | Nomograms | - |
dc.subject.keywordPlus | RISK STRATIFICATION | - |
dc.subject.keywordPlus | DIAGNOSTIC-ACCURACY | - |
dc.subject.keywordPlus | BIOPSY | - |
dc.subject.keywordPlus | ANTIGEN | - |
dc.subject.keywordPlus | MEN | - |
dc.subject.keywordPlus | MRI | - |
dc.relation.journalResearchArea | Oncology | - |
dc.relation.journalWebOfScienceCategory | Oncology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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