ArticleSIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics2020
IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION.
Article in SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- PREDICT-GBM: A multicenter platform advancing personalized glioblastoma radiotherapy planning.NPJ digital medicine · 2026Article
- Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss.Nature communications · 2025Article
- Inverse Problem Regularization for 3D Multi-Species Tumor Growth Models.International journal for numerical methods in biomedical engineering · 2025Article
- Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology.ArXiv · 2025Article
- CLAIRE-Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications.Journal of imaging · 2022Article
- CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures.Journal of open source software · 2021Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
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Authors and funding
5 authors.
Funding
Abstract
We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, multiparametric magnetic resonance imaging (MRI) scan of a glioblastoma patient. Tumor growth models are typically nonlinear parabolic partial differential equations (PDEs). Thus, we have to generate a second snapshot to be able to extract significant information from a single patient snapshot. We create this two-snapshot scenario as follows. We use an atlas (an average of several scans of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of the patient. Then, using the patient scan and the atlas, we combine image-registration algorithms and parameter estimation algorithms to achieve a better estimate of the healthy patient scan and the tumor growth parameters that are consistent with the data. Our scheme is based on our recent work (Scheufele et al.,
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Registered trials
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