A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seongkeun | - |
dc.contributor.author | Byun, Jieun | - |
dc.contributor.author | Woo, Ji Young | - |
dc.date.accessioned | 2021-08-11T08:35:56Z | - |
dc.date.available | 2021-08-11T08:35:56Z | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2790 | - |
dc.description.abstract | Background: Approximately 20-50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20-30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/app10113854 | - |
dc.identifier.scopusid | 2-s2.0-85086128258 | - |
dc.identifier.wosid | 000543385900191 | - |
dc.identifier.bibliographicCitation | Applied Sciences-basel, v.10, no.11 | - |
dc.citation.title | Applied Sciences-basel | - |
dc.citation.volume | 10 | - |
dc.citation.number | 11 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | CANCER-SPECIFIC MORTALITY | - |
dc.subject.keywordPlus | DISEASE RECURRENCE | - |
dc.subject.keywordPlus | NOMOGRAM | - |
dc.subject.keywordPlus | BIOPSIES | - |
dc.subject.keywordPlus | SCORE | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordPlus | PROBABILITY | - |
dc.subject.keywordPlus | GUIDELINES | - |
dc.subject.keywordPlus | ANTIGEN | - |
dc.subject.keywordPlus | MEN | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | prostate cancer | - |
dc.subject.keywordAuthor | biochemical recurrence | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.