An image-based computational modeling approach for prediction of temperature distribution during photothermal therapy

Abstract

Nanoparticle-assisted photothermal therapy (NPTT) has recently renewed the interest of using hyperthermia in cancer therapy due to selective heating of tumor by utilizing light-responsive nanoparticles such as gold nanoparticles (AuNPs). Pre-treatment planning of NPTT can help to predict temperature distribution within the body in order to optimize the treatment parameters before the actual heating operation. The use of actual tumor geometry and nanoparticle distribution are key requirements for accurate prediction of temperature distribution during numerical calculations of the heat transfer process. This study attempts to develop a numerical modeling strategy for NPTT based on computed tomography (CT) imaging. To this end, CT26 colon tumor-bearing mice were injected with alginate-coated AuNPs (Au@Alg) and then underwent CT imaging. The tumor geometry and nanoparticle distribution map were obtained directly from CT image of the tumor and exported into a finite element simulation software for subsequent heat transfer modeling. The predicted temperature of the tumor from numerical modeling was found to be in reasonable agreement with the measured data from in vivo thermometry. This model has the potential to be used as a pre-treatment planning tool to design an individualized heating protocol for various tumor geometry before the actual heating treatment.