ArticleConvergent science physical oncology2018
A Brownian dynamics tumor progression simulator with application to glioblastoma.
Article in Convergent science physical oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses.Cancer research communications · 2026Article
- Mathematical modeling for glioblastoma treatment: scenario generation and validation for clinical patient counseling.Frontiers in oncology · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion.Communications engineering · 2024Article
- Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach.Bulletin of mathematical biology · 2023Article
- Glioblastoma cell invasion: Go? Grow? Yes.Neuro-oncology · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth.Frontiers in molecular biosciences · 2022Article
- Network Analysis Identifies Phase Transitions for Tumor With Interacting Cells.Frontiers in physiology · 2022Article
- Predicting Confined 1D Cell Migration from Parameters Calibrated to a 2D Motor-Clutch Model.Biophysical journal · 2020Article
- Dynamics of 3D carcinoma cell invasion into aligned collagen.Integrative biology : quantitative biosciences from nano to macro · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Tumor progression modeling offers the potential to predict tumor-spreading behavior to improve prognostic accuracy and guide therapy development. Common simulation methods include continuous reaction-diffusion (RD) approaches that capture mean spatio-temporal tumor spreading behavior and discrete agent-based (AB) approaches which capture individual cell events such as proliferation or migration. The brain cancer glioblastoma (GBM) is especially appropriate for such proliferation-migration modeling approaches because tumor cells seldom metastasize outside of the central nervous system and cells are both highly proliferative and migratory. In glioblastoma research, current RD estimates of proliferation and migration parameters are derived from computed tomography or magnetic resonance images. However, these estimates of glioblastoma cell migration rates, modeled as a diffusion coefficient, are approximately 1-2 orders of magnitude larger than single-cell measurements in animal models of this disease. To identify possible sources for this discrepancy, we evaluated the fundamental RD simulation assumptions that cells are point-like structures that can overlap. To give cells physical size (~10
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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.