An information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia

Magnetic nanoparticle hyperthermia is a promising cancer treatment that achieves localized tumor heating through exposure to alternating magnetic fields. However, its clinical translation is limited by uncertainties in thermophysical tissue properties, nanoparticle distribution, and intrinsic magnetic characteristics, which hinder accurate temperature prediction and treatment planning. This study presents an information-theoretic framework for optimal experimental design that systematically addresses these uncertainties to enhance the precision of hyperthermia protocols. By maximizing mutual information between measurable temperature elevations and uncertain model parameters, including tissue density, specific heat, thermal conductivity, blood perfusion rate, and nanoparticle anisotropy constant, the framework identifies optimal magnetic field amplitudes that improve parameter identifiability. A one-dimensional surrogate model of the Pennes bioheat equation, coupled with linear response theory for nanoparticle heating, enables efficient simulation of thermal outcomes across varying initial conditions and uncertainty scenarios. The method consistently converged to optimal magnetic field amplitudes across both single and multiple-uncertain-parameter cases for a wide range of initial conditions, including constant, random, oscillatory, and near-zero magnetic fields, demonstrating robustness with respect to diverse initial conditions. Independent Monte Carlo-based optimization and verification confirmed that the proposed mutual information framework consistently identifies the same optimal magnetic field protocols, demonstrating robustness with respect to the numerical integration strategy used to evaluate information gain. These findings highlight the framework’s potential as a rigorous and computationally efficient tool for data-driven, uncertainty-aware experimental design in magnetic nanoparticle hyperthermia. Its application may inform improved parameter estimation and guide more effective, individualized thermal treatment strategies in both preclinical studies and future clinical translation.

Journal article