0 294

Cited 1 times in

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

Authors
 Jee Soo Park  ;  Soo Beom Choi  ;  Hee Jung Kim  ;  Nam Hoon Cho  ;  Sang Wun Kim  ;  Young Tae Kim  ;  Eun Ji Nam  ;  Jai Won Chung  ;  Deok Won Kim 
Citation
 International Journal of Gynecological Cancer, Vol.26(1) : 104-113, 2016 
Journal Title
 International Journal of Gynecological Cancer 
ISSN
 1048-891X 
Issue Date
2016
MeSH
Biomarkers, Tumor/genetics* ; Blotting, Western ; Cystadenocarcinoma, Serous/classification ; Cystadenocarcinoma, Serous/diagnosis* ; Cystadenocarcinoma, Serous/genetics ; Cystadenocarcinoma, Serous/surgery ; Databases, Genetic ; Female ; Gene Expression Profiling* ; Gene Expression Regulation, Neoplastic ; Humans ; Immunoenzyme Techniques ; Machine Learning* ; Monitoring, Intraoperative/methods* ; Neoplasm Staging ; Neoplasms, Glandular and Epithelial/classification ; Neoplasms, Glandular and Epithelial/diagnosis* ; Neoplasms, Glandular and Epithelial/genetics ; Neoplasms, Glandular and Epithelial/surgery ; Ovarian Neoplasms/classification ; Ovarian Neoplasms/diagnosis* ; Ovarian Neoplasms/genetics ; Ovarian Neoplasms/surgery ; Predictive Value of Tests ; Prognosis ; RNA, Messenger/genetics ; Real-Time Polymerase Chain Reaction ; Reverse Transcriptase Polymerase Chain Reaction ; Support Vector Machine ; Survival Rate
Keywords
Ovarian tumor ; Microarray analysis ; Artificial intelligence ; Multicategory classification ; Borderline tumor
Abstract
OBJECTIVES: 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.
Full Text
http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&AN=00009577-201601000-00014&LSLINK=80&D=ovft
DOI
10.1097/IGC.0000000000000566
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
Yonsei Authors
김덕원(Kim, Deok Won)
김상운(Kim, Sang Wun) ORCID logo https://orcid.org/0000-0002-8342-8701
김영태(Kim, Young Tae) ORCID logo https://orcid.org/0000-0002-7347-1052
남은지(Nam, Eun Ji)
박지수(Park, Jee Soo) ORCID logo https://orcid.org/0000-0001-9976-6599
조남훈(Cho, Nam Hoon) ORCID logo https://orcid.org/0000-0002-0045-6441
Export
RIS (EndNote)
XLS (Excel)
XML
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/145522
사서에게 알리기
  feedback

qrcode

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

Browse