Nanorobots-Assisted In Vivo Computational Methodology for Tumor Lesion Exploitation and Lesion Boundary Exploration

An innovative in vivo computational model that reframes the challenge of early tumor detection as an optimization problem has been developed recently. In this scope, the tumor lesion is viewed as the optimal solution that to be found by the computing agents (i.e., nanorobots). The tumor-triggered biological gradient field provides the information for the real-time path planning and fitness evaluation of nanorobots. The main emphasis of previous work has been on engineering tumor-targeting methodologies that address the challenges imposed by in vivo biological conditions. In this paper, we investigate the issue of tumor lesion exploitation by controlling the motion of nanorobotic swarm, which aims to enhance the operational efficiency of nanorobots deployed for tumor lesion identification. Thus, an exploitation approach about the settings of nanorobots moving direction and step size is developed. Furthermore, inspired by the spontaneous motion property of nanorobots, we propose cooperation and competition mechanisms to overcome the challenge of lesion boundary exploration in this paper. Some computational simulations are performed to validate the efficacy of this novel methodology.