Scientists Develop Method to Detect Cancer Cells Based on Their Movement

Tokyo: In a discovery, researchers from Tokyo Metropolitan University have developed a technique to distinguish cancerous cells from healthy ones by analyzing how they move, achieving an accuracy of up to 94%. The findings, led by Professor Hiromi Miyoshi and published in PLOS ONE, could pave the way for new diagnostic methods and deepen scientific understanding of biological processes such as wound healing and tissue regeneration.

The team studied the behavior of malignant fibrosarcoma cells—cancer cells originating in fibrous connective tissue—alongside healthy fibroblast cells, which play a crucial role in tissue structure. Using phase-contrast microscopy, a widely used, label-free imaging technique, researchers were able to track the movements of individual cells in real-time without the need for any staining or modification. This method allowed the cells to behave more naturally, closer to their native state on a petri dish.

Through advanced image analysis, the researchers extracted the trajectories of the cells and focused on various motion characteristics. Key indicators included the “sum of turn angles,” which measured how much the cells curved as they moved, the frequency of shallow directional changes, and the overall speed of migration. These subtle motility differences, invisible to the naked eye, turned out to be powerful indicators of a cell’s identity.

When combined, the curvature of the path and frequency of shallow turns enabled the team to accurately determine whether a cell was cancerous or healthy in 94% of cases. This high level of accuracy marks a significant advancement in non-invasive cancer diagnostics.

The technique holds promise beyond just cancer detection. Since cell motility plays a fundamental role in various biological functions such as tissue growth and wound healing, this label-free, trajectory-based approach could contribute to a wide range of biomedical research.

The study opens up new avenues in the field of cancer diagnostics, offering a faster and less intrusive alternative to traditional methods, and reinforces the potential of motion analysis in understanding the behavior of cells in health and disease.