Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue

Yifeng Zeng, Sreyankar Nandy, Bin Rao, Shuying Li, Andrea R. Hagemann, Lindsay K. Kuroki, Carolyn McCourt, David G. Mutch, Matthew A. Powell, Ian S. Hagemann, Quing Zhu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Ovarian cancer is a heterogeneous disease at the molecular and histologic level. Optical coherence tomography (OCT) is able to map ovarian tissue optical properties and heterogeneity, which has been proposed as a feature to aid in diagnosis of ovarian cancer. In this manuscript, depth-resolved en face scattering maps of malignant ovaries, benign ovaries, and benign fallopian tubes obtained from 20 patients are provided to visualize the heterogeneity of ovarian tissues. Six features are extracted from histograms of scattering maps. All features are able to statistically distinguish benign from malignant ovaries. Two prediction models were constructed based on these features: a logistic regression model (LR) and a support vector machine (SVM). The optimal set of features is mean scattering coefficient and scattering map entropy. The LR achieved a sensitivity and specificity of 97.0% and 97.8%, and SVM demonstrated a sensitivity and specificity of 99.6% and 96.4%. Our initial results demonstrate the feasibility of using OCT as an “optical biopsy tool” for detecting the microscopic scattering changes associated with neoplasia in human ovarian tissue.

Original languageEnglish
Article numbere201900115
JournalJournal of Biophotonics
Volume12
Issue number11
DOIs
StatePublished - Nov 1 2019

Keywords

  • cancer prediction
  • optical coherence tomography
  • ovarian cancer
  • scattering coefficient map

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