A Validated Methodological Approach to Prove the Safety of Clinical Electromagnetic Induction Systems in Magnetic Hyperthermia

Simple Summary This study examines the application of magnetic nanoparticle hyperthermia (MNH), a cancer treatment technique that utilizes magnetic particles at the scale of nanometers and a controlled magnetic field to selectively heat and destroy cancer cells. The study focuses on a specific system, the Sarah Nanotechnology System, which combines these magnetic particles and a device that generates the magnetic field. The main goal is to ensure this treatment is safe for patients. We used a combination of real-world experiments and computer simulations to test how the system affects the body's temperature, particularly aiming to avoid overheating healthy tissues. We used a virtual human model to predict temperature changes during treatment. The findings are promising for safely using this advanced technology in cancer treatment, potentially offering a new, targeted approach for patients with advanced-stage tumors. This could be a significant step forward in cancer therapy, highlighting the importance of combining experimental and computational methods in medical research.Abstract The present study focuses on the development of a methodology for evaluating the safety of MNH systems, through the numerical prediction of the induced temperature rise in superficial skin layers due to eddy currents heating under an alternating magnetic field (AMF). The methodology is supported and validated through experimental measurements of the AMF's distribution, as well as temperature data from the torsos of six patients who participated in a clinical trial study. The simulations involved a computational model of the actual coil, a computational model of the cooling system used for the cooling of the patients during treatment, and a detailed human anatomical model from the Virtual Population family. The numerical predictions exhibit strong agreement with the experimental measurements, and the deviations are below the estimated combined uncertainties, confirming the accuracy of computational modeling. This study highlights the crucial role of simulations for translational medicine and paves the way for personalized treatment planning.