Abstract 5376: Label-free classification of cells based on supervised machine learning of subcellular structures for negative selection of circulating tumor cells
Authors: Kikuchi Hirotoshi , Hirotsu Amane , Ozaki Yusuke , Yamada Hidenao , Yamashita Daisuke , Okazaki Shigetoshi , Ueda Yukio , Matsumoto Tomohiro , Hiramatsu Yoshihiro , Kamiya Kinji , Morita Yoshifumi , Sakaguchi Takanori , Konno Hiroyuki , Takeuchi Hiroya 
Published: September 2020

Abstract
Background: Circulating tumor cells (CTCs) are used as a liquid biopsy target in the treatment of cancer patients and are important for personalized medicine. Most CTC detection technologies are based on their surface markers such as EpCAM and cytokeratin. However, those positive selection methodologies appear to count only some CTCs because invasive tumor cells tend to change their surface markers during progression. Because the vast majority of nucleated cells circulating in the bloodstream are white blood cells (WBCs), eliminating WBCs from circulation is an efficient method to purify CTCs by negative selection. In this study, we demonstrate the development of cell recognition by computer vision technologies for pattern recognition based on the morphological features of live cells.
Materials and methods: Blood samples obtained from healthy volunteers, and five cancer cell lines, SW480, DLD-1, HCT116, Panc-1, and HepG2 were imaged by quantitative phase microscopy (QPM), which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. WBC recognition algorithm was developed using computer vision technologies for pattern recognition based on the histogram-oriented gradient (HOG) features extracted from QPM images (QPIs).
Results: We observed WBCs and five cell line cells by QPM, extracted certain features from the obtained images as training data for pattern recognition, and created an algorithm to differentiate WBCs from cell lines. The obtained algorithm successfully differentiated WBCs from cell lines (AUC=0.996). This image recognition methods was then applied to QPIs of the cells flowing in the chamber, and properly differentiated WBCs from cell line cells flowing in the chamber (AUC=0.99). Finally, we applied this method to blood samples obtained from a healthy volunteer and patients with advanced cancer, and identified CTC fractions which were much higher in blood of a cancer patient than that of a healthy volunteer. In addition, in esophageal cancer patients who received neoadjuvant chemotherapy followed by surgery, CTC fractions changed over time consistently with therapeutic effects.
Conclusions: We developed a novel method for label-free image identification of live cells by computer vision technologies for pattern recognition based on the features of QPIs. Our novel method is expected to apply to detecting CTCs in a noncytotoxic manner, thus providing good opportunities for studying intact CTCs
Citation Format: Hirotoshi Kikuchi, Amane Hirotsu, Yusuke Ozaki, Hidenao Yamada, Daisuke Yamashita, Shigetoshi Okazaki, Yukio Ueda, Tomohiro Matsumoto, Yoshihiro Hiramatsu, Kinji Kamiya, Yoshifumi Morita, Takanori Sakaguchi, Hiroyuki Konno, Hiroya Takeuchi. Label-free classification of cells based on supervised machine learning of subcellular structures for negative selection of circulating tumor cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5376.