ArticleAnnals of biomedical engineering2019
Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI.
Article in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma patients.Physics in medicine and biology · 2026Article
- Anatomically aware simulation of patient-specific glioblastoma xenografts.PLoS computational biology · 2026Article
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth.Cancers · 2025Article
- Optimizing therapeutic outcomes with Mechanotherapy and Ultrasound Sonopermeation in solid tumors.PLoS computational biology · 2025Article
- 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
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion.Royal Society open science · 2024Article
- A data assimilation framework to predict the response of glioma cells to radiation.Mathematical biosciences and engineering : MBE · 2023Article
- Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.Advanced drug delivery reviews · 2022Review
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community.Radiation research · 2022Article
- A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy ParameterizedFrontiers in oncology · 2022Article
- 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
- Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.Cancers · 2021Review
- Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.Scientific reports · 2021Article
- Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy.Cancers · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The spatiotemporal variations in tumor vasculature inevitably alters cell proliferation and treatment efficacy. Thus, rigorous characterization of tumor dynamics must include a description of this phenomenon. We have developed a family of biophysical models of tumor growth and angiogenesis that are calibrated with diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast-enhanced (DCE-) MRI data to provide individualized tumor growth forecasts. Tumor and blood volume fractions were evolved using two, coupled partial differential equations consisting of proliferation, diffusion, and death terms. To evaluate these models, rats (n = 8) with C6 gliomas were imaged seven times. The tumor volume fraction was estimated using DW-MRI, while DCE-MRI provided estimates of the blood volume fraction. The first three time points were used to calibrate model parameters, which were then used to predict growth at the remaining four time points and compared directly to the measurements. The best performing model predicted tumor growth with less than 10.3% error in tumor volume and with less than 9.4% error at the voxel-level at all prediction time points. The best performing model resulted in less than 9.3% error in blood volume at the voxel-level. This pre-clinical study demonstrates the potential for image-based, mechanistic modeling of tumor growth and angiogenesis.
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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.