ArticleInternational journal of radiation oncology, biology, physics2018
Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.
Article in International journal of radiation oncology, biology, physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin.NPJ systems biology and applications · 2025Article
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites.PLoS computational biology · 2024Article
- Modeling of brain tumors usingHeliyon · 2024Review
- A data assimilation framework to predict the response of glioma cells to radiation.Mathematical biosciences and engineering : MBE · 2023Article
- Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse.iScience · 2022Article
- Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.Advanced drug delivery reviews · 2022Review
- A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy ParameterizedFrontiers in oncology · 2022Article
- Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.Frontiers in oncology · 2022Article
- Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2021Article
- Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.Nature protocols · 2021Review
- A time-resolved experimental-mathematical model for predicting the response of glioma cells to single-dose radiation therapy.Integrative biology : quantitative biosciences from nano to macro · 2021Article
- Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.Scientific reports · 2021Article
- Towards integration ofScientific reports · 2020Article
- Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling.Radiation oncology (London, England) · 2020Article
- Translating preclinical MRI methods to clinical oncology.Journal of magnetic resonance imaging : JMRI · 2019Review
- Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome.Journal of the Royal Society, Interface · 2019Article
- Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI.Annals of biomedical engineering · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy. METHODS AND MATERIALS: Post-radiation therapy response is modeled using a cell death model (M
resultsFor both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M
conclusionsThe results of this study indicate that for both doses, the M
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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.