Magnetic fluid hyperthermia for different breast categories: Effect of nanoparticle distribution patterns and...

Magnetic fluid hyperthermia (MFH) offers a targeted means of thermally degrading malignant tissue by impregnating superparamagnetic nanoparticles (SPMNPs) within a tumor and exciting them with an alternating magnetic field. Although numerical modeling has been attempted before to guide MFH treatment optimization, systematic studies jointly integrating specific tissue categories, clinically realistic nanoparticle distributions, and dynamic perfusion evolution remain absent from the literature, leaving a critical gap in personalized MFH treatment planning. To address this, we develop a finite-element framework that integrates realistic SPMNPs distribution functions, along with a perfusion model whose magnitude varies with thermally induced tissue damage, all embedded within the Pennes bioheat formulation. Using representative breast compositions and tumor types, we investigate five spatial SPMNP-distributions under magnetic field conditions constrained by the magnetic field-frequency/Hergt-Dutz (Hf) threshold. The simulations resolve transient temperature fields, quantify thermal injury, and assess treatment quality across fatty to highly dense breast categories. The results indicate that fat-rich tumors exhibit a faster temperature rise than muscle-dominant tumors, and that the relative ease of heating mirrors the proportion of adipose to fibroglandular tissue in the surrounding breast. For deeply situated lesions, the extent of healthy-tissue injury is governed more strongly by the duration of heating than by the peak temperature attained, underscoring the importance of temporal control in MFH. Among the investigated spatial SPMNP-patterns, the radial Gaussian distribution consistently delivers the most advantageous balance - enhancing energy deposition within the tumor while restraining damage to adjacent tissue. These findings offer valuable insights for tailored, patient-specific hyperthermia therapy, thus lending to the successful clinical implementation of MFH as a promising tool for localized breast cancer treatment.