Early-Stage Cancer Biomarker Detection via Intravascular Nanomachines: Modeling and Analysis
This addresses early cancer detection for patients, but it is incremental as it improves modeling of existing nanomachine approaches.
This study tackled the problem of early-stage cancer biomarker detection by modeling intravascular nanomachines, finding that realistic vascular transport mechanisms reduce detection probability, with capillaries achieving the highest detection probability across nanomachine sizes.
Early detection of cancer is essential for timely diagnosis and improved patient outcomes. Among emerging technologies, intra-body nanoscale communication offers an innovative solution to identify molecular cues within the human bloodstream. This study investigates a minimally invasive approach for early-stage cancer biomarker detection using nanomachines introduced into the bloodstream. To assess the feasibility of this approach, computational simulations are used to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions. Current modeling approaches often fail to capture essential vascular characteristics, including non-uniform flow structures, size-dependent particle mobility, and particle margination driven by red blood cell interactions. To address these limitations, our study incorporates these factors into the simulation framework and quantifies their individual and combined effects on biomarker detection efficiency. Baseline detection performance is first obtained under uniform flow assumptions, after which introducing realistic vascular transport mechanisms progressively reduces detection probability for all vessel types and nanomachine sizes. Among the considered vessels, capillary consistently achieves the highest detection probability across all nanomachine sizes.