DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Y | - |
dc.contributor.author | Park, Y.H | - |
dc.contributor.author | Lee, J.Y | - |
dc.contributor.author | Choi, I.Y | - |
dc.contributor.author | Yu, H. | - |
dc.date.accessioned | 2017-07-19T12:46:20Z | - |
dc.date.available | 2017-07-19T12:46:20Z | - |
dc.date.created | 2016-08-16 | - |
dc.date.issued | 2016-07-18 | - |
dc.identifier.issn | 1472-6947 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/36415 | - |
dc.description.abstract | Background: Prostate specific antigen (PSA) is an important biomarker to monitor the response to the treatment, but has not been fully utilized as a whole sequence. We used a longitudinal biomarker PSA to discover a new prognostic pattern that predicts castration-resistant prostate cancer (CRPC) after androgen deprivation therapy. Methods: We transformed the longitudinal PSA into a discrete sequence, used frequent sequential pattern mining to find candidate patterns from the sequences, and selected the most predictive and informative pattern among the candidates. Results: Patients were less likely to be CRPC if, after PSA values reach nadir, the PSA decreases more than 0.048 ng/ml during a month, and the decrease occurs again. This pattern significantly increased the accuracy of predicting CRPC by supplementing information provided by existing PSA patterns such as pretreatment PSA. Conclusions: This result can help clinicians to stratify men by the risk of CRPC and to determine the patient that needs intensive follow-up. | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | BMC Medical Informatics and Decision Making | - |
dc.title | Discovery of Prostate Specific Antigen Pattern to Predict Castration Resistant Prostate Cancer of Androgen Deprivation Therapy | - |
dc.type | Article | - |
dc.identifier.doi | 10.1186/S12911-016-0297-0 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | BMC Medical Informatics and Decision Making, v.16, no.SUPPL 1, pp.63 | - |
dc.identifier.wosid | 000393278900005 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.number | SUPPL 1 | - |
dc.citation.startPage | 63 | - |
dc.citation.title | BMC Medical Informatics and Decision Making | - |
dc.citation.volume | 16 | - |
dc.contributor.affiliatedAuthor | Yu, H. | - |
dc.identifier.scopusid | 2-s2.0-84978380790 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 3 | - |
dc.description.scptc | 3 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.subject.keywordPlus | RECURRENCE | - |
dc.subject.keywordPlus | SURVIVAL | - |
dc.subject.keywordPlus | STATISTICS | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordAuthor | Prostate specific antigen | - |
dc.subject.keywordAuthor | Longitudinal biomarker | - |
dc.subject.keywordAuthor | Frequent sequential pattern mining | - |
dc.subject.keywordAuthor | Prediction | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Medical Informatics | - |
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