A critical review on magnetic fluid hyperthermia treatment for cancers: Challenges and pathways from computation to clinic.

Standard therapeutic practices for malignancies have often been limited by recurrent challenges, particularly in advanced-stage cancers or following prolonged treatment exposure. Hyperthermia treatment, in conjunction with standard therapies, has been found to yield favorable results. With rapid advancement in the research for the development and synthesis of superparamagnetic nanoparticles (SPMNP), Magnetic Fluid Hyperthermia (MFH) has established itself as a promising platform for therapeutic intervention in malignant tumors. However, the development of reliable and effective numerical models is a domain that demands extensive research to accurately evaluate the dosimetric data of SPMNP to be infused, its mass transfer to and distribution within the analyzed domain, its cytotoxic effects on biological systems, generated heat flux under the influence of an Alternating magnetic field (AMF), rise in temperature and heat transfer within the tissue and lastly, the resultant damage caused to both malignant and adjoining healthy tissues. With this backdrop, one cannot overemphasize the role of modelling, and in vitro and in vivo experiments involving MFH. For accurate characterization of MFH and effective treatment outcome estimation, it is important to take into consideration the salient tissue-specific physiological parameters. All these physiological parameters, which in general have non-linear behavior are of prime important in forecasting treatment outcomes and its damage potential to the adjoining healthy tissue, which in turn ensures the acceptability of the treatment as a mainstream clinical procedure. This article presents an in-depth review of the available various biocompatible SPMNPs and their injection strategies, and heating capacity under the influence of an AMF. It also discusses the different classical Fourier law-based bio heat transfer models that are used for numerical study to predict the temperature profile of the ROI, their dependency on various bio-physical parameters of the tissue as well as magneto-physical parameters of the SPMNPs and AMF, and the capability of the models to reliably estimate tissue damage. The hindrances faced by the treatment procedures towards converting MFH into mainstream clinical trials is also discussed with a futuristic vision of integrating artificial intelligence and machine learning approach towards optimizing the MFH parameters.