In silico modelling of tumour margin diffusion and infiltration: review of current status.
As a result of advanced treatment techniques, requiring precise target definitions, a need for more accurate delineation of the Clinical Target Volume (CTV) has arisen. Mathematical modelling is found to be a powerful tool to provide fairly accurate predictions for the Microscopic Extension (ME) of a tumour to be incorporated in a CTV. In general terms, biomathematical models based on a sequence of observations or development of a hypothesis assume some links between biological mechanisms involved in cancer development and progression to provide quantitative or qualitative measures of tumour behaviour as well as tumour response to treatment. Generally, two approaches are taken: deterministic and stochastic modelling. In this paper, recent mathematical models, including deterministic and stochastic methods, are reviewed and critically compared. It is concluded that stochastic models are more promising to provide a realistic description of cancer tumour behaviour due to being intrinsically probabilistic as well as discrete, which enables incorporation of patient-specific biomedical data such as tumour heterogeneity and anatomical boundaries.
Department of Medical Physics, Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000, Australia. fatemeh.moghaddasi@adelaide.edu.au
Fatemeh Leyla
FL
Moghaddasi
Eva
E
Bezak
Loredana
L
Marcu
D002454:Cell Differentiation; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004247:DNA / Q000378:metabolism; D004058:Diffusion; D005109:Extracellular Matrix / Q000378:metabolism; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D009154:Mutation; D009369:Neoplasms / Q000473:pathology*; D011336:Probability; D020266:Radiotherapy, Conformal / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods; D013269:Stochastic Processes
D004247:DNA