ArticleIEEE transactions on medical imaging2005
Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation.
Article in IEEE transactions on medical imaging, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.
What it found
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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.
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Who cites it
106 citing papers in PubMed, 356 citations in OpenAlex.
- Roadmap to Local Tumour Growth: Insights from Cervical Cancer.Scientific reports · 2019Trial
- A data assimilation framework for predicting the spatiotemporal response of high-grade gliomas to chemoradiation.BMC cancer · 2025Article
- Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology.ArXiv · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- The influence of anisotropy on the clinical target volume of brain tumor patients.Physics in medicine and biology · 2024Article
- Post-operative glioblastoma cancer cell distribution in the peritumoural oedema.Frontiers in oncology · 2024Article
- Modelling microtube driven invasion of glioma.Journal of mathematical biology · 2023Article
- A position- and time-dependent pressure profile to model viscoelastic mechanical behavior of the brain tissue due to tumor growth.Computer methods in biomechanics and biomedical engineering · 2023Article
- 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
- Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?Cancers · 2022Article
- Modelling glioma progression, mass effect and intracranial pressure in patient anatomy.Journal of the Royal Society, Interface · 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
- 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
- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2021Article
- Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan.IEEE transactions on medical imaging · 2021Article
- Towards integration ofScientific reports · 2020Article
- Decompressive craniectomy of post-traumatic brain injury: an in silico modelling approach for intracranial hypertension management.Scientific reports · 2020Article
- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
We propose a new model to simulate the three-dimensional (3-D) growth of glioblastomas multiforma (GBMs), the most aggressive glial tumors. The GBM speed of growth depends on the invaded tissue: faster in white than in gray matter, it is stopped by the dura or the ventricles. These different structures are introduced into the model using an atlas matching technique. The atlas includes both the segmentations of anatomical structures and diffusion information in white matter fibers. We use the finite element method (FEM) to simulate the invasion of the GBM in the brain parenchyma and its mechanical interaction with the invaded structures (mass effect). Depending on the considered tissue, the former effect is modeled with a reaction-diffusion or a Gompertz equation, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. The tumor growth simulation is assessed by comparing the in-silico GBM growth with the real growth observed on two magnetic resonance images (MRIs) of a patient acquired with 6 mo difference. Results show the feasibility of this new conceptual approach and justifies its further evaluation.
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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.