title: Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: *Department of Medical Engineering, Yonsei University College of Medicine; †Department of Medicine, Yonsei University College of Medicine, Seoul, Korea; ‡Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea; and Department of §Obstetrics and Gynecology and ∥Pathology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Jee Soo
authors.initials: JS
authors.lastname: Park
authors.forename: Soo Beom
authors.initials: SB
authors.lastname: Choi
authors.forename: Hee Jung
authors.initials: HJ
authors.lastname: Kim
authors.forename: Nam Hoon
authors.initials: NH
authors.lastname: Cho
authors.forename: Sang Wun
authors.initials: SW
authors.lastname: Kim
authors.forename: Young Tae
authors.initials: YT
authors.lastname: Kim
authors.forename: Eun Ji
authors.initials: EJ
authors.lastname: Nam
authors.forename: Jai Won
authors.initials: JW
authors.lastname: Chung
authors.forename: Deok Won
authors.initials: DW
authors.lastname: Kim
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D015153:Blotting, Western; D000077216:Carcinoma, Ovarian Epithelial; D018284:Cystadenocarcinoma, Serous / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000601:surgery; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007124:Immunoenzyme Techniques; D000069550:Machine Learning; D016343:Monitoring, Intraoperative / Q000379:methods*; D009367:Neoplasm Staging; D009375:Neoplasms, Glandular and Epithelial / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000601:surgery; D010051:Ovarian Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000601:surgery; D011237:Predictive Value of Tests; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics; D060888:Real-Time Polymerase Chain Reaction; D020133:Reverse Transcriptase Polymerase Chain Reaction; D060388:Support Vector Machine; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D012333:RNA, Messenger