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Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective
DC Field | Value | Language |
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dc.contributor.author | Wee, Chan Woo | - |
dc.contributor.author | Jang, Bum-Sup | - |
dc.contributor.author | Kim, Jin Ho | - |
dc.contributor.author | Jeong, Chang Wook | - |
dc.contributor.author | Kwak, Cheol | - |
dc.contributor.author | Kim, Hyun Hoe | - |
dc.contributor.author | Ku, Ja Hyeon | - |
dc.contributor.author | Kim, Seung Hyup | - |
dc.contributor.author | Cho, Jeong Yeon | - |
dc.contributor.author | Kim, Sang Youn | - |
dc.date.accessioned | 2022-06-24T08:26:45Z | - |
dc.date.available | 2022-06-24T08:26:45Z | - |
dc.date.created | 2022-05-13 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | Cancer Research and Treatment, Vol.54 No.1, pp.234-244 | - |
dc.identifier.issn | 1598-2998 | - |
dc.identifier.uri | https://hdl.handle.net/10371/184078 | - |
dc.description.abstract | Copyright © 2022 by the Korean Cancer Association.Purpose This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b). Materials and Methods A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation. Results According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p < 0.001), percentage of positive biopsy cores (PPC) (β=0.033, p < 0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p < 0.001), and cTMRI (β=0.259, p < 0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p < 0.001), PPC (β=0.024, p < 0.001), Gleason score (β=0.753, p < 0.001), and cTMRI (β=0.507, p < 0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall area under the receiver operating characteristic curve (AUC)/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74, p=0.060) and SVI (0.88 vs. 0.84, p < 0.001). Conclusion Two models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists. | - |
dc.language | 영어 | - |
dc.publisher | 대한암학회 | - |
dc.title | Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective | - |
dc.type | Article | - |
dc.identifier.doi | 10.4143/CRT.2020.1221 | - |
dc.citation.journaltitle | Cancer Research and Treatment | - |
dc.identifier.wosid | 000792610700002 | - |
dc.identifier.scopusid | 2-s2.0-85123614385 | - |
dc.citation.endpage | 244 | - |
dc.citation.number | 1 | - |
dc.citation.startpage | 234 | - |
dc.citation.volume | 54 | - |
dc.identifier.kciid | ART002802703 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Jeong, Chang Wook | - |
dc.contributor.affiliatedAuthor | Kwak, Cheol | - |
dc.contributor.affiliatedAuthor | Ku, Ja Hyeon | - |
dc.contributor.affiliatedAuthor | Cho, Jeong Yeon | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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