ArticleCell proliferation2009
Hybrid mathematical model of glioma progression.
Article in Cell proliferation, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- A dynamical model of combination therapy applied to glioma.Journal of biological physics · 2022Article
- Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model.Biomechanics and modeling in mechanobiology · 2022Article
- Spatiotemporal dynamics of a glioma immune interaction model.Scientific reports · 2021Article
- An Integrative and Modular Framework to Recapitulate Emergent Behavior in Cell Migration.Frontiers in cell and developmental biology · 2020Article
- Glioma growth modeling based on the effect of vital nutrients and metabolic products.Medical & biological engineering & computing · 2018Article
- A simple and accurate rule-based modeling framework for simulation of autocrine/paracrine stimulation of glioblastoma cell motility and proliferation by L1CAM in 2-D culture.BMC systems biology · 2017Article
- Non-standard radiotherapy fractionations delay the time to malignant transformation of low-grade gliomas.PloS one · 2017Article
- Differentiated cell behavior: a multiscale approach using measure theory.Journal of mathematical biology · 2015Article
- A mathematical model of pre-diagnostic glioma growth.Journal of theoretical biology · 2015Article
- Glioma follow white matter tracts: a multiscale DTI-based model.Journal of mathematical biology · 2015Article
- Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model.PloS one · 2015Article
- Oedema-based model for diffuse low-grade gliomas: application to clinical cases under radiotherapy.Cell proliferation · 2014Article
- Computer implementation of a new therapeutic model for GBM tumor.Computational and mathematical methods in medicine · 2014Article
- Mathematical modelling of spatio-temporal glioma evolution.Theoretical biology & medical modelling · 2013Article
- Dynamic density functional theory of solid tumor growth: Preliminary models.AIP advances · 2012Article
- Improving the time-machine: estimating date of birth of grade II gliomas.Cell proliferation · 2012Article
- Mathematical model of the role of intercellular signalling in intercellular cooperation during tumorigenesis.Cell proliferation · 2011Article
- Coupled mathematical model of tumorigenesis and angiogenesis in vascular tumours.Cell proliferation · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesGliomas are an important form of brain cancer, with high mortality rate. Mathematical models are often used to understand and predict their behaviour. However, using current modeling techniques one must choose between simulating individual cell behaviour and modeling tumours of clinically significant size. MATERIALS AND
methodsWe propose a hybrid compartment-continuum-discrete model to simulate glioma growth and malignant cell invasion. The discrete portion of the model is capable of capturing intercellular interactions, including cell migration, intercellular communication, spatial cell population heterogeneity, phenotype differentiation, epigenetic events, proliferation, and apoptosis. Combining this with a compartment and continuum model allows clinically significant tumour sizes to be evaluated. RESULTS AND
conclusionsThis model is used to perform multiple simulations to determine sensitivity to changes in important model parameters, specifically, the fundamental length parameter, necrotic cell degradation rate, rate of cell migration, and rate of phenotype transformation. Using these values, the model is able to simulate tumour growth and invasion behaviour, observed clinically. This mathematical model provides a means to simulate various tumour development scenarios, which may lead to a better understanding of how altering fundamental parameters can influence neoplastic progression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.