Early-Stage Cancer Biomarker Detection via Intravascular Nanomachines: Modeling and Analysis

TL;DR

This paper models and analyzes the feasibility of using intravascular nanomachines for early cancer biomarker detection, emphasizing realistic vascular transport effects on detection efficiency.

Contribution

It introduces an advanced simulation framework that incorporates vascular flow complexities, improving the accuracy of nanomachine-based biomarker detection modeling.

Findings

Detection probability decreases with realistic vascular effects.

Capillaries offer the highest detection probability.

Vascular flow characteristics significantly impact detection performance.

Abstract

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.