ArticleMethods in molecular biology (Clifton, N.J.)2018
Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details.
Article in Methods in molecular biology (Clifton, N.J.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 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
- Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis.Scientific reports · 2025Article
- Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy.bioRxiv : the preprint server for biology · 2025Article
- Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth.Cancers · 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
- MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens.NPJ digital medicine · 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
- Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition.Journal of computational science · 2024Article
- Designing clinical trials for patients who are not average.iScience · 2024Review
- Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes.Brain multiphysics · 2023Article
- Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells.Science advances · 2023Article
- Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting.Journal of computational science · 2023Article
- Ensemble Inversion for Brain Tumor Growth Models With Mass Effect.IEEE transactions on medical imaging · 2023Article
- MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.Cancer research · 2022Article
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Modelling glioma progression, mass effect and intracranial pressure in patient anatomy.Journal of the Royal Society, Interface · 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
- Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.Nature protocols · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.
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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.