Research Article
Smart Tumor Homing for Manhattan-Like Capillary Network Regulated Tumor Microenvironment
@INPROCEEDINGS{10.1007/978-3-030-92163-7_11, author={Yin Qing and Yue Sun and Yue Xiao and Yifan Chen}, title={Smart Tumor Homing for Manhattan-Like Capillary Network Regulated Tumor Microenvironment}, proceedings={Bio-Inspired Information and Communications Technologies. 13th EAI International Conference, BICT 2021, Virtual Event, September 1--2, 2021, Proceedings}, proceedings_a={BICT}, year={2022}, month={1}, keywords={Tumor homing Manhattan-like BGFs CGD iterative algorithm}, doi={10.1007/978-3-030-92163-7_11} }- Yin Qing
Yue Sun
Yue Xiao
Yifan Chen
Year: 2022
Smart Tumor Homing for Manhattan-Like Capillary Network Regulated Tumor Microenvironment
BICT
Springer
DOI: 10.1007/978-3-030-92163-7_11
Abstract
This paper investigates the tumor microenvironment regulated by the dense interconnected capillary network nearby, forming Manhattan-like biological gradient fields (BGFs) distribution. The research to date has tended to focus on modeling in Euclidean space rather than Manhattan space. Based on the Manhattan-like BGFs, we propose a coordinate gradient descent (CGD) iterative algorithm to realize tumor homing. Nanorobots with contrast agents and sensors are employed as computing agents. The sensors serve as local sensing agents to provide iterative information, and the contrast agents can deposit themselves on the tumor through the enhanced permeability and retention (EPR) to make magnetic resonance imaging (MRI) easier to detect. We aim to achieve tumor homing using as few iterations as possible in a Manhattan-like BGF. The simulation results show that the proposed CGD algorithm has higher efficiency and fewer iterations in Manhattan-like BGFs than the brute-fore.
