ArticlePhysics in medicine and biology2012
Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth.
Article in Physics in medicine and biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes.Brain multiphysics · 2023Article
- Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation.Scientific reports · 2022Article
- Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?Cancers · 2022Article
- Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.Nature protocols · 2021Review
- Initial Condition Assessment for Reaction-Diffusion Glioma Growth Models: A Translational MRI-Histology (In)Validation Study.Tomography (Ann Arbor, Mich.) · 2021Article
- Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.Scientific reports · 2021Article
- Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.iScience · 2020Review
- Towards integration ofScientific reports · 2020Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling.Radiation oncology (London, England) · 2020Article
- Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases.Medical physics · 2019Article
- An adjoint-based method for a linear mechanically-coupled tumor model: Application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging.Computational mechanics · 2019Article
- Mathematical models of tumor cell proliferation: A review of the literature.Expert review of anticancer therapy · 2018Review
- Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.International journal of radiation oncology, biology, physics · 2018Article
- Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details.Methods in molecular biology (Clifton, N.J.) · 2018Article
- A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predictJournal of the Royal Society, Interface · 2017Article
- Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.Computer methods in applied mechanics and engineering · 2017Article
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6 authors.
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Abstract
We build on previous work to show how serial diffusion-weighted MRI (DW-MRI) data can be used to estimate proliferation rates in a rat model of brain cancer. Thirteen rats were inoculated intracranially with 9L tumor cells; eight rats were treated with the chemotherapeutic drug 1,3-bis(2-chloroethyl)-1-nitrosourea and five rats were untreated controls. All animals underwent DW-MRI immediately before, one day and three days after treatment. Values of the apparent diffusion coefficient (ADC) were calculated from the DW-MRI data and then used to estimate the number of cells in each voxel and also for whole tumor regions of interest. The data from the first two imaging time points were then used to estimate the proliferation rate of each tumor. The proliferation rates were used to predict the number of tumor cells at day three, and this was correlated with the corresponding experimental data. The voxel-by-voxel analysis yielded Pearson’s correlation coefficients ranging from −0.06 to 0.65, whereas the region of interest analysis provided Pearson’s and concordance correlation coefficients of 0.88 and 0.80, respectively. Additionally, the ratio of positive to negative proliferation values was used to separate the treated and control animals (p <0.05) at an earlier point than the mean ADC values. These results further illustrate how quantitative measurements of tumor state obtained non-invasively by imaging can be incorporated into mathematical models that predict tumor growth.
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