In Vivo Computing for Smart Tumor Targeting in Taxicab-Geometry Vasculature

This paper investigates the tumor microenvironment regulated by densely interconnected capillaries, resulting in the distribution of tumor-induced biological gradient field (BGF) in taxicab-geometry vasculature (TGV). We aim to improve the efficiency of tumor targeting with the knowledge of BGF in TGV, which is facilitated by a swarm of magnetic nanorobots. An external system observes and records the nanorobot swarm (NS) reaction to the BGF. Then the NS is controlled to move toward the potential tumor location by an external magnetic field. In this way, the BGF formed under the constraint of TGV is the objective function to be optimized, where the tumor center corresponds to the maximum value. The high-risk tissue area is the domain of the objective function, while the NS plays the role of a computing agent. Subsequently, we propose the coordinate gradient descent (CGD) targeting strategy for NS steering. This strategy estimates the BGF in the direction perpendicular to the propagation direction of NS to improve the efficiency of tumor detection. In addition, it considers the limited lifespan of NS in vivo, where a memory step-size mechanism (MSM) is utilized to reduce the targeting time. We use computational experiments to show that the CGD strategy yields higher tumor-targeting probabilities than the brute-force search and the original gradient-descent-inspired targeting strategy for the BGF subject to TGV.