0 309

Cited 1 times in

Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning

DC FieldValueLanguage
dc.contributor.author조남훈-
dc.contributor.author김덕원-
dc.contributor.author김상운-
dc.contributor.author김영태-
dc.contributor.author남은지-
dc.contributor.author박지수-
dc.date.accessioned2017-01-19T13:00:56Z-
dc.date.available2017-01-19T13:00:56Z-
dc.date.issued2016-
dc.identifier.issn1048-891X-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/145522-
dc.description.abstractOBJECTIVES: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diagnosing SBOTs with accuracy of 48% to 79%. Using DNA microarray technology, we designed multicategory classification models to support frozen-section diagnosis within 30 minutes. MATERIALS AND METHODS: We systematically evaluated 6 machine learning algorithms and 3 feature selection methods using 5-fold cross-validation and a grid search on microarray data obtained from the National Center for Biotechnology Information. To validate the models and selected biomarkers, expression profiles were analyzed in tissue samples obtained from the Yonsei University College of Medicine. RESULTS: The best accuracy of the optimal machine learning model was 97.3%. In addition, 5 features, including the expression of the putative biomarkers SNTN and AOX1, were selected to differentiate between normal, SBOT, and serous ovarian carcinoma groups. Different expression levels of SNTN and AOX1 were validated by real-time quantitative reverse-transcription polymerase chain reaction, Western blotting, and immunohistochemistry. A multinomial logistic regression model using SNTN and AOX1 alone was used to construct a simple-to-use equation that gave a diagnostic test accuracy of 91.9%. CONCLUSIONS: We identified 2 biomarkers, SNTN and AOX1, that are likely involved in the pathogenesis and progression of ovarian tumors. An accurate diagnosis of ovarian tumor subclasses by application of the equation in conjunction with expression analysis of SNTN and AOX1 would offer a new accurate diagnosis tool in conjunction with frozen-section diagnosis within 30 minutes.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherLippincott Williams & Wilkins-
dc.relation.isPartOfInternational Journal of Gynecological Cancer-
dc.subject.MESHBiomarkers, Tumor/genetics*-
dc.subject.MESHBlotting, Western-
dc.subject.MESHCystadenocarcinoma, Serous/classification-
dc.subject.MESHCystadenocarcinoma, Serous/diagnosis*-
dc.subject.MESHCystadenocarcinoma, Serous/genetics-
dc.subject.MESHCystadenocarcinoma, Serous/surgery-
dc.subject.MESHDatabases, Genetic-
dc.subject.MESHFemale-
dc.subject.MESHGene Expression Profiling*-
dc.subject.MESHGene Expression Regulation, Neoplastic-
dc.subject.MESHHumans-
dc.subject.MESHImmunoenzyme Techniques-
dc.subject.MESHMachine Learning*-
dc.subject.MESHMonitoring, Intraoperative/methods*-
dc.subject.MESHNeoplasm Staging-
dc.subject.MESHNeoplasms, Glandular and Epithelial/classification-
dc.subject.MESHNeoplasms, Glandular and Epithelial/diagnosis*-
dc.subject.MESHNeoplasms, Glandular and Epithelial/genetics-
dc.subject.MESHNeoplasms, Glandular and Epithelial/surgery-
dc.subject.MESHOvarian Neoplasms/classification-
dc.subject.MESHOvarian Neoplasms/diagnosis*-
dc.subject.MESHOvarian Neoplasms/genetics-
dc.subject.MESHOvarian Neoplasms/surgery-
dc.subject.MESHPredictive Value of Tests-
dc.subject.MESHPrognosis-
dc.subject.MESHRNA, Messenger/genetics-
dc.subject.MESHReal-Time Polymerase Chain Reaction-
dc.subject.MESHReverse Transcriptase Polymerase Chain Reaction-
dc.subject.MESHSupport Vector Machine-
dc.subject.MESHSurvival Rate-
dc.titleIntraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning-
dc.typeArticle-
dc.publisher.locationUnited States-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Pathology-
dc.contributor.googleauthorJee Soo Park-
dc.contributor.googleauthorSoo Beom Choi-
dc.contributor.googleauthorHee Jung Kim-
dc.contributor.googleauthorNam Hoon Cho-
dc.contributor.googleauthorSang Wun Kim-
dc.contributor.googleauthorYoung Tae Kim-
dc.contributor.googleauthorEun Ji Nam-
dc.contributor.googleauthorJai Won Chung-
dc.contributor.googleauthorDeok Won Kim-
dc.identifier.doi10.1097/IGC.0000000000000566-
dc.contributor.localIdA03812-
dc.contributor.localIdA00376-
dc.contributor.localIdA00526-
dc.contributor.localIdA00729-
dc.contributor.localIdA01262-
dc.relation.journalcodeJ01115-
dc.identifier.pmid26512784-
dc.identifier.urlhttp://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&AN=00009577-201601000-00014&LSLINK=80&D=ovft-
dc.subject.keywordOvarian tumor-
dc.subject.keywordMicroarray analysis-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordMulticategory classification-
dc.subject.keywordBorderline tumor-
dc.contributor.alternativeNameCho, Nam Hoon-
dc.contributor.alternativeNameKim, Deok Won-
dc.contributor.alternativeNameKim, Sang Wun-
dc.contributor.alternativeNameKim, Young Tae-
dc.contributor.alternativeNameNam, Eun Ji-
dc.contributor.affiliatedAuthorCho, Nam Hoon-
dc.contributor.affiliatedAuthorKim, Deok Won-
dc.contributor.affiliatedAuthorKim, Sang Wun-
dc.contributor.affiliatedAuthorKim, Young Tae-
dc.contributor.affiliatedAuthorNam, Eun Ji-
dc.citation.volume26-
dc.citation.number1-
dc.citation.startPage104-
dc.citation.endPage113-
dc.identifier.bibliographicCitationInternational Journal of Gynecological Cancer, Vol.26(1) : 104-113, 2016-
dc.date.modified2017-01-16-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Obstetrics and Gynecology (산부인과학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.