Literature DB >> 10606418

Mathematical models of tumour and normal tissue response.

B Jones1, R G Dale.   

Abstract

The historical application of mathematics in the natural sciences and in radiotherapy is compared. The various forms of mathematical models and their limitations are discussed. The Linear Quadratic (LQ) model can be modified to include (i) radiobiological parameter changes that occur during fractionated radiotherapy, (ii) situations such as focal forms of radiotherapy, (iii) normal tissue responses, and (iv) to allow for the process of optimization. The inclusion of a variable cell loss factor in the LQ model repopulation term produces a more flexible clonogenic doubling time, which can simulate the phenomenon of 'accelerated repopulation'. Differential calculus can be applied to the LQ model after elimination of the fraction number integers. The optimum dose per fraction (maximum cell kill relative to a given normal tissue fractionation sensitivity) is then estimated from the clonogen doubling times and the radiosensitivity parameters (or alpha/beta ratios). Economic treatment optimization is described. Tumour volume studies during or following teletherapy are used to optimize brachytherapy. The radiation responses of both individual tumours and tumour populations (by random sampling 'Monte-Carlo' techniques from statistical ranges of radiobiological and physical parameters) can be estimated. Computerized preclinical trials can be used to guide choice of dose fractionation scheduling in clinical trials. The potential impact of gene and other biological therapies on the results of radical radiotherapy are testable. New and experimentally testable hypotheses are generated from limited clinical data by exploratory modelling exercises.

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Year:  1999        PMID: 10606418     DOI: 10.1080/028418699432572

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  22 in total

1.  Optimal solution for a cancer radiotherapy problem.

Authors:  A Bertuzzi; C Bruni; F Papa; C Sinisgalli
Journal:  J Math Biol       Date:  2013-01       Impact factor: 2.259

Review 2.  Linear quadratic and tumour control probability modelling in external beam radiotherapy.

Authors:  S F C O'Rourke; H McAneney; T Hillen
Journal:  J Math Biol       Date:  2008-09-30       Impact factor: 2.259

3.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.

Authors:  R Rockne; J K Rockhill; M Mrugala; A M Spence; I Kalet; K Hendrickson; A Lai; T Cloughesy; E C Alvord; K R Swanson
Journal:  Phys Med Biol       Date:  2010-05-18       Impact factor: 3.609

Review 4.  External beam techniques to boost cervical cancer when brachytherapy is not an option-theories and applications.

Authors:  Omar Mahmoud; Sarah Kilic; Atif J Khan; Sushil Beriwal; William Small
Journal:  Ann Transl Med       Date:  2017-05

5.  Cellular Response to Exponentially Increasing and Decreasing Dose Rates: Implications for Treatment Planning in Targeted Radionuclide Therapy.

Authors:  Jay H Solanki; Thomas Tritt; Jordan B Pasternack; Julia J Kim; Calvin N Leung; Jason D Domogauer; Nicholas W Colangelo; Venkat R Narra; Roger W Howell
Journal:  Radiat Res       Date:  2017-05-25       Impact factor: 2.841

6.  A mathematical model for brain tumor response to radiation therapy.

Authors:  R Rockne; E C Alvord; J K Rockhill; K R Swanson
Journal:  J Math Biol       Date:  2008-09-25       Impact factor: 2.259

7.  Radioembolisation with 90Y-microspheres: dosimetric and radiobiological investigation for multi-cycle treatment.

Authors:  Marta Cremonesi; Mahila Ferrari; Mirco Bartolomei; Franco Orsi; Guido Bonomo; Demetrio Aricò; Andrew Mallia; Concetta De Cicco; Guido Pedroli; Giovanni Paganelli
Journal:  Eur J Nucl Med Mol Imaging       Date:  2008-07-10       Impact factor: 9.236

8.  Optimal weekly scheduling in fractionated radiotherapy: effect of an upper bound on the dose fraction size.

Authors:  C Bruni; F Conte; F Papa; C Sinisgalli
Journal:  J Math Biol       Date:  2014-08-29       Impact factor: 2.259

9.  Applying a 4D multiscale in vivo tumor growth model to the exploration of radiotherapy scheduling: the effects of weekend treatment gaps and p53 gene status on the response of fast growing solid tumors.

Authors:  Dimitra D Dionysiou; Georgios S Stamatakos
Journal:  Cancer Inform       Date:  2007-02-16

10.  Introduction of hypermatrix and operator notation into a discrete mathematics simulation model of malignant tumour response to therapeutic schemes in vivo. Some operator properties.

Authors:  Georgios S Stamatakos; Dimitra D Dionysiou
Journal:  Cancer Inform       Date:  2009-10-21
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