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

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

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)104-113
Number of pages10
JournalInternational Journal of Gynecological Cancer
Issue number1
Publication statusPublished - 2016 Jan 1

Bibliographical note

Publisher Copyright:
© 2015 by IGCS AND ESGO.

All Science Journal Classification (ASJC) codes

  • Oncology
  • Obstetrics and Gynaecology


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