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.

中文翻译：

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出租车几何血管中智能肿瘤靶向的体内计算


本文研究了由密集互连的毛细血管调节的肿瘤微环境，从而导致肿瘤诱导的生物梯度场（BGF）在出租车几何脉管系统（TGV）中的分布。我们的目标是利用 TGV 中 BGF 的知识来提高肿瘤靶向的效率，这是由一群磁性纳米机器人促进的。外部系统观察并记录纳米机器人群 (NS) 对 BGF 的反应。然后通过外部磁场控制NS向潜在的肿瘤位置移动。这样，TGV约束下形成的BGF就是待优化的目标函数，其中肿瘤中心对应最大值。高危组织区域是目标函数的域，而NS则扮演计算代理的角色。随后，我们提出了 NS 转向的坐标梯度下降（CGD）目标策略。该策略估计垂直于NS传播方向的BGF，以提高肿瘤检测的效率。此外，考虑到NS在体内的有限寿命，利用记忆步长机制（MSM）来减少靶向时间。我们使用计算实验表明，对于接受 TGV 的 BGF，CGD 策略比强力搜索和原始梯度下降启发的靶向策略产生更高的肿瘤靶向概率。