Integrated microfluidic platform for the capture and identification of circulating tumor cells using dielectrophoresis and image recognition.

BACKGROUND: Circulating tumor cells (CTCs), which detach from primary tumors and enter the peripheral blood, serve as biomarkers for monitoring a patient's status following cancer therapy. METHODS: In this study, we developed a microfluidic platform integrating dielectrophoresis and image recognition for the capture and detection of CTCs from blood sample. Experimental parameters included applied electric field strengths ranging from 12.5 to 32.5 × 104 V/m (at 1 MHz) and sample flow rates of 6.25-31.25 mm/min. Fluorescent antibodies were employed to label captured CTCs for real-time detection. Furthermore, a detection software utilizing OpenCV was developed to label CTCs through image processing. The identification performance of the self-developed software was evaluated and compared with machine learning algorithms. RESULTS AND CONCLUSIONS: Results demonstrated that the optimal CTC separation efficiency of 78 ± 2% was achieved at an electric field of 25 × 104 V/m and a flow rate of 18.75 mm/min. While the self-developed software achieved a lower recall than the YOLOv8 model (85 ± 4% vs. 92 ± 2%), its precision matched that of YOLOv8 (90 ± 3% vs. 89 ± 4%).