Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of Machine Learning Algorithms with Microfluidic Technologies

Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant challenges for detection. Microfluidic systems, or “lab-on-a-chip” devices, have emerged as a revolutionary tool in liquid biopsy, enabling efficient isolation and analysis of CTCs. These systems offer advantages such as reduced sample volume, enhanced sensitivity, and the ability to integrate multiple processes into a single platform. Several microfluidic techniques, including size-based filtration, dielectrophoresis, and immunoaffinity capture, have been developed to enhance CTC detection. The integration of machine learning (ML) with microfluidic systems has further improved the specificity and accuracy of CTC detection, significantly advancing the speed and efficiency of early cancer diagnosis. ML models have enabled more precise analysis of CTCs by automating detection processes and enhancing the ability to identify rare and heterogeneous cell populations. These advancements have already demonstrated their potential in improving diagnostic accuracy and enabling more personalized treatment approaches. In this review, we highlight the latest progress in the integration of microfluidic technologies and ML algorithms, emphasizing how their combination has changed early cancer diagnosis and contributed to significant advancements in this field.