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A mathematical model of tumor growth and its response to single irradiation

Overview of attention for article published in Theoretical Biology and Medical Modelling, February 2016
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Title
A mathematical model of tumor growth and its response to single irradiation
Published in
Theoretical Biology and Medical Modelling, February 2016
DOI 10.1186/s12976-016-0032-7
Pubmed ID
Authors

Yoichi Watanabe, Erik L. Dahlman, Kevin Z. Leder, Susanta K. Hui

Abstract

Mathematical modeling of biological processes is widely used to enhance quantitative understanding of bio-medical phenomena. This quantitative knowledge can be applied in both clinical and experimental settings. Recently, many investigators began studying mathematical models of tumor response to radiation therapy. We developed a simple mathematical model to simulate the growth of tumor volume and its response to a single fraction of high dose irradiation. The modelling study may provide clinicians important insights on radiation therapy strategies through identification of biological factors significantly influencing the treatment effectiveness. We made several key assumptions of the model. Tumor volume is composed of proliferating (or dividing) cancer cells and non-dividing (or dead) cells. Tumor growth rate (or tumor volume doubling time) is proportional to the ratio of the volumes of tumor vasculature and the tumor. The vascular volume grows slower than the tumor by introducing the vascular growth retardation factor, θ. Upon irradiation, the proliferating cells gradually die over a fixed time period after irradiation. Dead cells are cleared away with cell clearance time. The model was applied to simulate pre-treatment growth and post-treatment radiation response of rat rhabdomyosarcoma tumors and metastatic brain tumors of five patients who were treated with Gamma Knife stereotactic radiosurgery (GKSRS). By selecting appropriate model parameters, we showed the temporal variation of the tumors for both the rat experiment and the clinical GKSRS cases could be easily replicated by the simple model. Additionally, the application of our model to the GKSRS cases showed that the α-value, which is an indicator of radiation sensitivity in the LQ model, and the value of θ could be predictors of the post-treatment volume change. The proposed model was successful in representing both the animal experimental data and the clinically observed tumor volume changes. We showed that the model can be used to find the potential biological parameters, which may be able to predict the treatment outcome. However, there is a large statistical uncertainty of the result due to the small sample size. Therefore, a future clinical study with a larger number of patients is needed to confirm the finding.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
Unknown 126 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 28%
Student > Bachelor 17 13%
Researcher 14 11%
Student > Master 10 8%
Other 9 7%
Other 19 15%
Unknown 22 17%
Readers by discipline Count As %
Engineering 18 14%
Medicine and Dentistry 15 12%
Biochemistry, Genetics and Molecular Biology 13 10%
Physics and Astronomy 12 9%
Mathematics 12 9%
Other 27 21%
Unknown 30 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 February 2016.
All research outputs
#5,518,189
of 7,306,657 outputs
Outputs from Theoretical Biology and Medical Modelling
#124
of 178 outputs
Outputs of similar age
#198,447
of 282,711 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#3
of 5 outputs
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So far Altmetric has tracked 178 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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