Literature DB >> 22006625

Are more complicated tumour control probability models better?

Jiafen Gong1, Mairon M Dos Santos, Chris Finlay, Thomas Hillen.   

Abstract

Mathematical models for the tumour control probability (TCP) are used to estimate the expected success of radiation treatment protocols of cancer. There are several TCP models in the literature, from the simplest (Poissonian TCP) to the well-advanced stochastic birth-death processes. Simple and complex models often make the same predictions. Hence, here, we present a systematic study where we compare six of these TCP models: the Poisson TCP, the Zaider-Minerbo TCP, a Monte Carlo TCP and their corresponding cell cycle (two-compartment) models. Several clinical non-uniform treatment protocols for prostate cancer are employed to evaluate these models. These include fractionated external beam radiotherapies, and high and low dose rate brachytherapies. We find that in realistic treatment scenarios, all one-compartment models and all two-compartment models give basically the same results. A difference occurs between one-compartment and two-compartment models due to reduced radiosensitivity of quiescent cells.We find that care must be taken for the right choice of parameters, such as the radiosensitivities α and β and the hazard function h. Typically, different hazard functions are used for fractionated treatment (fractionated survival fraction) and for brachytherapies (Lea-Catcheside protraction factor). We were able to combine these two approaches into one 'effective' hazard function. Based on our results, we can recommend the use of the Poissonian TCP for everyday treatment planning. More complicated models should only be used when absolutely necessary.

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Year:  2011        PMID: 22006625     DOI: 10.1093/imammb/dqr023

Source DB:  PubMed          Journal:  Math Med Biol        ISSN: 1477-8599            Impact factor:   1.854


  8 in total

1.  A reaction-diffusion model for radiation-induced bystander effects.

Authors:  Oluwole Olobatuyi; Gerda de Vries; Thomas Hillen
Journal:  J Math Biol       Date:  2016-12-29       Impact factor: 2.259

2.  Effects of G2-checkpoint dynamics on low-dose hyper-radiosensitivity.

Authors:  Oluwole Olobatuyi; Gerda de Vries; Thomas Hillen
Journal:  J Math Biol       Date:  2018-04-20       Impact factor: 2.259

3.  Prediction of Tumor Control in 90Y Radioembolization by Logit Models with PET/CT-Based Dose Metrics.

Authors:  Yuni K Dewaraja; Theresa Devasia; Ravi K Kaza; Justin K Mikell; Dawn Owen; Peter L Roberson; Matthew J Schipper
Journal:  J Nucl Med       Date:  2019-05-30       Impact factor: 10.057

4.  Stochastic model for tumor control probability: effects of cell cycle and (a)symmetric proliferation.

Authors:  Andrew Dhawan; Kamran Kaveh; Mohammad Kohandel; Sivabal Sivaloganathan
Journal:  Theor Biol Med Model       Date:  2014-11-22       Impact factor: 2.432

5.  Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer.

Authors:  Marcin Paczkowski; Warren W Kretzschmar; Bostjan Markelc; Stanley K Liu; Leoni A Kunz-Schughart; Adrian L Harris; Mike Partridge; Helen M Byrne; Pavitra Kannan
Journal:  Commun Biol       Date:  2021-01-04

6.  Simulation of Gamma-Ray Transmission Buildup Factors for Stratified Spherical Layers.

Authors:  Abdulrahman A Alfuraih
Journal:  Dose Response       Date:  2022-02-17       Impact factor: 2.658

7.  Hyper-radiosensitivity affects low-dose acute myeloid leukemia incidence in a mathematical model.

Authors:  Sjors Stouten; Ben Balkenende; Lars Roobol; Sjoerd Verduyn Lunel; Christophe Badie; Fieke Dekkers
Journal:  Radiat Environ Biophys       Date:  2022-07-21       Impact factor: 2.017

8.  Mathematical optimization of the combination of radiation and differentiation therapies for cancer.

Authors:  Jeff W N Bachman; Thomas Hillen
Journal:  Front Oncol       Date:  2013-03-18       Impact factor: 6.244

  8 in total

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