Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

Towards Realistic 3D Models of Tumor Vascular Networks

For reliable in silico or in vitro investigations in, for example, biosensing and drug delivery applications, accurate models of tumor vascular networks down to the capillary size are essential. Compared to images acquired with conventional medical imaging techniques, digitalized histological tumor slices have a higher resolution, enabling the delineation of capillaries. Volume rendering procedures can then be used to generate a 3D model. However, the preparation of such slices leads to misalignments in relative slice orientation between consecutive slices. Thus, image registration algorithms are necessary to re-align the slices. Here, we present an algorithm for the registration and reconstruction of a vascular network from histologic slices applied to 169 tumor slices. The registration includes two steps. First, consecutive images are incrementally pre-aligned using feature- and area-based transformations. Second, using the previous transformations, parallel registration for all images is enabled. Combining intensity- and color-based thresholds along with heuristic analysis, vascular structures are segmented. A 3D interpolation technique is used for volume rendering. This results in a 3D vascular network with approximately 400-450 vessels with diameters down to 25-30 µm. A delineation of vessel structures with close distance was limited in areas of high structural density. Improvement can be achieved by using images with higher resolution and or machine learning techniques.

Tension-Relaxation In Vivo Computing Principle for Tumor Sensitization and Targeting

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy.

In Vivo Computing Strategies for Tumor Sensitization and Targeting

Several evolution strategies for in vivo computation are proposed with the aim of realizing tumor sensitization and targeting (TST) by externally manipulable nanoswimmers. In such targeting systems, nanoswimmers assembled by magnetic nanoparticles are externally manipulated to search for the tumor in the high-risk tissue by a rotating magnetic field produced by a coil system. This process can be interpreted as in vivo computation, where the tumor in the high-risk tissue corresponds to the global maximum or minimum of the in vivo optimization problem, the nanoswimmers are seen as the computational agents, the tumor-triggered biological gradient field (BGF) is used for fitness evaluation of the agents, and the high-risk tissue is the search space. Considering that the state-of-the-art magnetic nanoswimmer control method can only actuate all the nanoswimmers heading in the same direction simultaneously, we introduce the orthokinetic movement strategies into the agent location updating in the existing swarm intelligence algorithms. Especially, the gravitational search algorithm (GSA) is revisited and the corresponding in vivo optimization algorithm called orthokinetic GSA (OGSA) is proposed to carry out the TST. Furthermore, to determine the direction of the orthokinetic agent movement in every iteration of the operation, we propose several strategies according to the fitness ranking of the nanoswimmers in the BGF. To verify the superiority of the OGSA and choose the optimal evolution strategy, some numerical experiments are presented and compared with that of the brute-force search, which represents the traditional method for TST. It is found that the TST performance can be improved by the weak priority evolution strategy (WP-ES) in most of the scenarios.

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.

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the "natural" computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumor-triggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the "natural" computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the "brute-force" search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as magnetic nanorobots, a two-dimensional microchannel network as the human vasculature, and a magnetic nanorobotic control system as the external actuating and tracking system. Results from the in silico simulations and in vitro experiments verify the effectiveness of the proposed Se-TS for two representative BGF landscapes.

Self-Regulated and Co-ordinated Smart Tumor Homing for Complex Vascular Networks

Inspired by the collaborative movements of the living cells in different natural biological processes, this paper highlights a self-regulated and co-ordinated targeting strategy to detect tumor in complex human vasculature. We show by computational experiments that simple and non-centralized nanoparticles with limited locomotive capability, when they collaborate and move in the form of groups, can be very robust in the tumor homing process. Once reaching tumor location, such nanoparticles (potential contrast agents) can deposit themselves on tumor through the enhanced permeability and retention (EPR) effect thereby making the malignant cancerous area more prominent. Hence, existing medical imaging techniques with limited resolution can be used to detect small malignant tumors. We believe that our work will help in early cancer detection through an independent, non-centralized and self-controlled system.Clinical Relevance:- With the help of target amplification through contrast agents such as self-regulated nanoparticles contrast enhanced medical imaging can detect the tumor at its initial stages when 90% of the cancers are curable.

Perspective: Computational Nanobiosensing

With the development of nanotechnology, externally manipulable or self-regulatable smart nanosystems can be utilized as effective tools for computational nanobiosensing, where natural computing strategies are exploited for knowledge-aided nanobiosensing.