↓ Skip to main content

Towards Predicting the Response of a Solid Tumour to Chemotherapy and Radiotherapy Treatments: Clinical Insights from a Computational Model

Overview of attention for article published in PLoS Computational Biology, July 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
15 X users
patent
1 patent

Citations

dimensions_citation
100 Dimensions

Readers on

mendeley
95 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Towards Predicting the Response of a Solid Tumour to Chemotherapy and Radiotherapy Treatments: Clinical Insights from a Computational Model
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003120
Pubmed ID
Authors

Gibin G. Powathil, Douglas J. A. Adamson, Mark A. J. Chaplain

Abstract

In this paper we use a hybrid multiscale mathematical model that incorporates both individual cell behaviour through the cell-cycle and the effects of the changing microenvironment through oxygen dynamics to study the multiple effects of radiation therapy. The oxygenation status of the cells is considered as one of the important prognostic markers for determining radiation therapy, as hypoxic cells are less radiosensitive. Another factor that critically affects radiation sensitivity is cell-cycle regulation. The effects of radiation therapy are included in the model using a modified linear quadratic model for the radiation damage, incorporating the effects of hypoxia and cell-cycle in determining the cell-cycle phase-specific radiosensitivity. Furthermore, after irradiation, an individual cell's cell-cycle dynamics are intrinsically modified through the activation of pathways responsible for repair mechanisms, often resulting in a delay/arrest in the cell-cycle. The model is then used to study various combinations of multiple doses of cell-cycle dependent chemotherapies and radiation therapy, as radiation may work better by the partial synchronisation of cells in the most radiosensitive phase of the cell-cycle. Moreover, using this multi-scale model, we investigate the optimum sequencing and scheduling of these multi-modality treatments, and the impact of internal and external heterogeneity on the spatio-temporal patterning of the distribution of tumour cells and their response to different treatment schedules.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Italy 1 1%
Germany 1 1%
Spain 1 1%
United States 1 1%
Unknown 89 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 29%
Researcher 20 21%
Student > Master 7 7%
Student > Doctoral Student 5 5%
Student > Bachelor 4 4%
Other 16 17%
Unknown 15 16%
Readers by discipline Count As %
Mathematics 21 22%
Medicine and Dentistry 12 13%
Engineering 9 9%
Physics and Astronomy 8 8%
Computer Science 7 7%
Other 21 22%
Unknown 17 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 13 October 2020.
All research outputs
#3,191,486
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#2,816
of 8,964 outputs
Outputs of similar age
#26,222
of 206,624 outputs
Outputs of similar age from PLoS Computational Biology
#25
of 104 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 206,624 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.