Two-Dimensional Light Scattering Anisotropy Cytometry for Label-Free Classification of Ovarian Cancer Cells via Machine Learning.

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology
PMID: 31313517

Abstract

We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label-free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label-free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry.

Authors

  • Xuantao Su
    Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Tao Yuan
    Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Zhiwen Wang
    Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China.
  • Kun Song
    Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.
  • Rongrong Li
    School of Economics and Management, China University of Petroleum (East China), Qingdao, 266580, People's Republic of China.
  • Cunzhong Yuan
    Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.
  • Beihua Kong
    Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, China.

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