Optically Induced Dielectrophoresis and Machine Learning Algorithms for the Identification of the Circulating Tumor Cells
Detecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiven...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3900/97/1/71 |
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| Summary: | Detecting circulating tumor cells (CTCs) is a challenge in cancer research. Their dissemination into the blood stream represents a crucial event in the formation of the metastases from the primary tumor. For this reason, targeting CTCs in human liquid biopsies is a warning event for cancer invasiveness, progression, and prognosis. In this regard, by means of the optically induced dielectrophoresis (ODEP) technique, we investigated the response to the electric field, at different frequencies, of human prostatic carcinoma PC3 cells, which mimic CTCs derived from prostate cancer, and human leukemia monocytic THP-1 cells, which simulate circulating monocytes. The obtained spectra of the cell motion descriptors represent the unique identification signature of each cell type. |
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| ISSN: | 2504-3900 |