Lab-On-Chip Platform to Expedite Cancer Diagnoses

Cancer was responsible for nearly 10 million deaths in 2020, accounting for almost one in every six deaths worldwide. Timely cancer diagnosis remains a major challenge, as abnormal cellular growth is often detected too late. Early diagnosis is critical, and recent research has focused on detecting rare circulating tumor cells (CTCs) in peripheral blood as noninvasive markers for diagnosis. However, isolating target cells for examination is inherently difficult. Traditional methods typically require complex sample preparation, substantial equipment, and large sample volumes, and even then, it remains challenging to efficiently separate the cells.

Researchers from K. N. Toosi University of Technology (Tehran, Iran) have now introduced a groundbreaking system that uses standing surface acoustic waves to separate CTCs from red blood cells with remarkable precision and efficiency. The system developed by the team integrates advanced computational modeling, experimental analysis, and artificial intelligence (AI) algorithms to analyze complex acoustofluidic phenomena. By combining machine learning algorithms with data-driven modeling, they were able to fine-tune the system for optimal recovery and cell separation rates. The platform, described in the journal Physics of Fluids, achieves 100% recovery under ideal conditions, while significantly reducing energy consumption through precise control of acoustic pressures and flow rates.

While many methods for enriching particles through microfluidics have been developed, those using acoustofluidics stand out due to their biocompatibility, ability to generate high-force magnitudes at MPa pressure ranges, and production of cell-scale wavelengths. The researchers' novel approach incorporates dualized pressure acoustic fields, which enhance the impact on target cells, and positions them strategically at critical points in the microchannel geometry on a lithium niobate substrate. By applying acoustic pressure within the microchannel, the system generates reliable datasets that reveal cell interaction times and trajectory patterns, helping to predict tumor cell migration.

“We have produced an advanced, lab-on-chip platform that enables real-time, energy-efficient, and highly accurate cell separation,” said researcher Afshin Kouhkord. “The technology promises to improve CTC separation efficiency and open new possibilities for earlier and more effective cancer diagnosis. It also paves the way for microengineering and applied AI in personalized medicine and cancer diagnostics.”

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