Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration

Abstract

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging modalities. Recent advancements in material engineering have enabled the development of nanoparticles tailored for various imaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). These nanoscale agents improve sensitivity and specificity, enabling early cancer detection and precise tumor characterization. Monte Carlo (MC) simulations play a pivotal role in optimizing nanomaterial-based imaging by modeling their interactions with biological tissues, predicting contrast enhancement, and refining dosimetry for radiation-based imaging techniques. These computational methods provide valuable insights into nanoparticle behavior, aiding in the design of more effective imaging agents. Moreover, artificial intelligence (AI) and machine learning (ML) approaches are transforming cancer imaging by enhancing image reconstruction, automating segmentation, and improving diagnostic accuracy. AI-driven models can also optimize MC-based simulations by accelerating data analysis and refining nanoparticle design through predictive modeling. This review explores the latest advancements in nanomaterial-based cancer imaging, highlighting the synergy between nanotechnology, MC simulations, and AI-driven innovations. By integrating these interdisciplinary approaches, future cancer imaging technologies can achieve unprecedented precision, paving the way for more effective diagnostics and personalized treatment strategies.

Keywords: CT; MRI; Monte Carlo simulation; PAI; PET; US; artificial intelligence; cancer therapy; machine learning; medical imaging; nanomaterials; nanoparticles.

Figure 1

Roles of nanoparticles in nanomedicine for cancer imaging and therapy. Reproduced from reference [9] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 2

Advantages of using nanoparticles in cancer theranostics. Reproduced from reference [16] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 3

In vivo visualization of firefly luciferase-transduced myoblasts labeled with SPIONs. T2-weighted MR images were obtained from a mouse injected intracardially with SPION-labeled cells (A) and a control mouse (B). Arrows indicate the hypointense area at the injection site. Bioluminescent imaging was performed 5 (C) and 12 (D) days post-cell administration, showing distinct bioluminescent activity (left) compared to the control mouse (right). Reproduced from reference [26] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 4

Schematic of MRI-driven motion control procedures. MRI tracking sequences monitor head motion during PET scanning. When head motion exceeds a threshold, it marks actual movement and sets a framing boundary for recombining PET images. This divides PET data into discrete temporal subunits with fixed head poses. This motion control for PET data is completed by assembling these framed PET images into a single PET image. Reproduced from reference [27] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 5

Percentage contrast enhancement for portal imaging with various nanoparticle materials and concentrations using 6 MV (a) flattening-filter (FF) and (b) flattening-filter-free (FFF) photon beams. Nanoparticles of gold (Au), platinum (Pt), iodine (I), silver (Ag), and iron oxide (Fe₂O₃) at concentrations of 3, 7, 18, 30, and 40 mg/mL were used. The percentage contrast enhancement represents the increase in imaging contrast ratio when nanoparticles were added to the tumor. Reproduced from reference [52] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 6

Representation of an AuNP with some possible functional groups. Reproduced from reference [65] under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/ (accessed on 17 March 2025)).

Figure 7

The potential mechanisms of AuNP radiosensitization. Reproduced from reference [66] under the Creative Commons Attribution 3.0 International License (https://creativecommons.org/licenses/by/3.0/ (accessed on 17 March 2025)).