Cancer remains one of the world's deadliest diseases, responsible for nearly 10 million deaths in 2020 alone, according to the World Health Organization. Early detection is crucial for improving survival rates, yet timely diagnoses are often hindered by the difficulty of identifying abnormal cellular growth before it becomes advanced. Recent advances in medical research have focused on detecting rare circulating tumour cells (CTCs) in peripheral blood. These cells, which shed from primary tumours, serve as noninvasive biomarkers that could potentially revolutionize cancer diagnosis. However, the process of isolating these cells from the bloodstream remains a significant challenge. Traditional methods require elaborate sample preparation, specialized equipment, and large sample volumes—making it difficult to efficiently separate and identify these key cells. In a breakthrough study published in Physics of Fluids by AIP Publishing, two researchers from K. N. Toosi University of Technology in Tehran, Iran, have introduced a novel lab-on-chip platform that uses standing surface acoustic waves to separate CTCs from red blood cells with unprecedented precision and efficiency. This innovative system combines advanced computational modeling, experimental analysis, and artificial intelligence algorithms to optimize the separation process. "We've integrated machine learning with computational modeling to fine-tune our system for optimal recovery and cell separation," said Naser Naserifar, one of the researchers. "The system achieves 100% recovery of CTCs under optimal conditions, all while significantly reducing energy consumption by precisely controlling acoustic pressures and flow rates." The researchers' approach utilizes acoustofluidics—using sound waves to manipulate fluids at the microscale—which has emerged as a promising solution due to its biocompatibility and ability to generate high-force magnitudes at MPa pressure ranges. This method applies dual pressure acoustic fields at strategic locations within the microchannel on a lithium niobate substrate, doubling the impact on target cells. The design creates reliable datasets that reveal cell interactions and movement patterns, which can help predict tumour cell migration. "Our platform enables real-time, energy-efficient, and highly accurate cell separation," said Afshin Kouhkord, the study's co-author. "The technology not only promises to enhance the efficiency of CTC separation, but it also opens new doors for earlier and more effective cancer diagnosis." The new system represents a major leap forward in microengineering and personalized medicine. It paves the way for more accessible and efficient cancer diagnostics, offering hope for earlier detection and more tailored treatment plans for patients. As the technology continues to evolve, it could play a pivotal role in transforming how we detect and treat cancer on a global scale.
