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A methodology for determining optimal thermal damage in magnetic nanoparticle hyperthermia cancer treatment
Author(s) -
Mital Manu,
Tafreshi Hooman V.
Publication year - 2012
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.1456
Subject(s) - bioheat transfer , hyperthermia , flatness (cosmology) , hyperthermia treatment , hyperthermia therapy , cancer treatment , materials science , biological system , exponential function , magnetic nanoparticles , computer science , thermal , nanoparticle , mathematical optimization , mathematics , cancer , physics , nanotechnology , biology , mathematical analysis , thermodynamics , genetics , quantum mechanics , meteorology , cosmology
SUMMARY Hyperthermia treatment of tumors uses localized heating to damage cancer cells and can also be utilized to increase the efficacy of other treatment methods such as chemotherapy. Magnetic nanoparticle hyperthermia is one of the least invasive techniques of delivering heat. It is based on injecting magnetic nanoparticles into the tumor and subjecting them to an alternating magnetic field. The technique is aimed at damaging the tumor without affecting the surrounding healthy tissue. In this preliminary study, we consider a simplified model (two concentric spheres that represent the tumor and its surrounding tissues) that employs a numerical solution of the Pennes bioheat equation. The model assumes a Gaussian distribution for the spatial variation of the applied thermal energy and an exponential decay function for the time variation. The objective of the study is to optimize the parameters that control the spatial and the time variation of the thermal energy. The optimization process is performed by formulating a fitness function that rewards damage in the region representing the tumor but penalizes damage in the surrounding tissues. Because of the flatness of this fitness function near the optimum, a genetic algorithm is used as the optimization method for its robust non‐gradient‐based approach. The overall aim of this work is to propose a methodology that can be used for hyperthermia treatment in a clinical scenario. Copyright © 2011 John Wiley & Sons, Ltd.