Abstract 6692: Identification of circulating tumor cells based on machine learning

Abstract Circulating tumor cells (CTC) are tumor cells that have shed into the bloodstream from primary tumor and circulate in the blood. CTC could provide better understanding of tumor metastasis and show the possibility as promising biomarker for early detection of cancer, cancer prognosis, noninvasive monitoring of treatment response, and personalized medicine. However, the isolation and characterization has been a major technological challenge due to their rareness among the massive blood cells. CytoGen’s Smart BiopsyTM CTC platform comprised of CTC isolator, IF (Immunofluorescence) Stainer, and Cell Image Analyzer is a unique and competitive technology platform for the isolation and analysis of CTC from blood. Intact live CTC could be isolated gravity-based filtration via high-density micro-porous (HDM) chip with minimized cellular damage and high retrieval rate. For the more accurate and quick analysis of CTC after isolation with Smart BiopsyTM CTC platform, we are developing the analysis method of CTC identification using machine learning technologies. Preliminary results of machine learning showed the accurate separation of CTC and PBMC (peripheral blood mononuclear cells) based on the specific markers of them. CTC identification based on machine learning technologies might be reliable analysis method and give secure information of CTC. Citation Format: Jae Hyuk Lee, Sung Ho Park, Jihoon Kang, Jihyun Lee, Soee Kim, Jungwon Kim, Young Woong Sohn, Jong Kil Lee. Identification of circulating tumor cells based on machine learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6692.