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Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Печать: 0278-940X
ISSN Онлайн: 1943-619X

Выпуски:
Том 47, 2019 Том 46, 2018 Том 45, 2017 Том 44, 2016 Том 43, 2015 Том 42, 2014 Том 41, 2013 Том 40, 2012 Том 39, 2011 Том 38, 2010 Том 37, 2009 Том 36, 2008 Том 35, 2007 Том 34, 2006 Том 33, 2005 Том 32, 2004 Том 31, 2003 Том 30, 2002 Том 29, 2001 Том 28, 2000 Том 27, 1999 Том 26, 1998 Том 25, 1997 Том 24, 1996 Том 23, 1995

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2014011825
pages 383-417

A Mathematical Framework for Minimally Invasive Tumor Ablation Therapies

Sheldon K. Hall
Institute of Biomedical Engineering, University of Oxford, Oxford, UK
Ean Hin Ooi
School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia
Stephen J. Payne
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK

Краткое описание

Minimally invasive tumor ablations (MITAs) are an increasingly important tool in the treatment of solid tumors across multiple organs. The problems experienced in modeling different types of MITAs are very similar, but the development of mathematical models is mostly performed in isolation according to modality. Fundamental research into the modeling of specific types of MITAs is indeed required, but to choose the optimal treatment for an individual the primary clinical requirement is to have reliable predictions for a range of MITAs.
In this review of the mathematical modeling of MITAs 4 modalities are considered: radiofrequency ablation, microwave ablation, cryoablation, and irreversible electroporation. The similarities in the mathematical modeling of these treatments are highlighted, and the analysis of the models within a general framework is discussed. This will aid in developing a deeper understanding of the sensitivity of MITA models to physiological parameters and the impact of uncertainty on predictions of the ablation zone.
Through robust validation and analysis of the models it will be possible to choose the best model for a given application. This is important because many different models exist with no objective comparison of their performance. The collection of relevant in vivo experimental data is also critical to parameterize such models accurately. This approach will be necessary to translate the field into clinical practice.


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