Researchers A. Sila Okcu, M. Etem Bas, and Ozgur B. Akan from the Middle East Technical University in Turkey have published a study exploring the physical limits of detecting cancer using tumor-derived extracellular vesicles (EVs). Their work, titled “Physical Limits of Proximal Tumor Detection via MAGE-A Extracellular Vesicles,” was published in the journal Physical Review Applied.
Early cancer detection is crucial for effective treatment, but current methods often rely on invasive tissue biopsies or liquid biopsies that can be limited by biomarker dilution. The researchers focused on EVs, which are small particles released by tumor cells and carry biomarkers like melanoma-associated antigen-A (MAGE-A). These EVs are highly concentrated in the space immediately surrounding a tumor, making them a promising target for near-field detection.
The study used a combination of particle-based Brownian dynamics simulations and reaction-diffusion partial differential equations to model the transport of EVs and the detection process. The researchers found that at micrometer scales, EV transport is governed by random diffusion, which can lead to a low number of EVs being present at any given time and place. This increases the risk of false negatives, where the detection method fails to identify the presence of a tumor.
To address this, the researchers proposed a smart-needle sensor designed to detect MAGE-A-positive microvesicles near a tumor. They formulated detection as a threshold-based binary hypothesis test, which involves setting a threshold for the number of EVs detected within a certain time frame. If the number of EVs detected exceeds this threshold, the test concludes that a tumor is present.
The study found that the maximum feasible detection radius for such a sensor is approximately 275 micrometers, with a sensing window of 6000 seconds. This means that the sensor would need to be placed very close to the tumor to reliably detect the presence of EVs. The results outline the physical limits of proximal EV-based detection and provide valuable insights for the design of minimally invasive peri-tumoral sensors.
While this research is primarily focused on medical applications, the principles of near-field detection and the use of smart sensors could have broader implications for the energy industry. For example, similar detection methods could be used to monitor the integrity of pipelines or other infrastructure by detecting trace amounts of specific molecules or particles that indicate the presence of leaks or other issues. Additionally, the use of Brownian dynamics simulations and reaction-diffusion models could be applied to study the transport of fluids and particles in various energy systems, helping to optimize their design and operation.
This article is based on research available at arXiv.