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.