ReviewAdvanced drug delivery reviews2022
Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.
Review in Advanced drug delivery reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Unlocking glioblastoma: breakthroughs in molecular mechanisms and next-generation therapies.Medical oncology (Northwood, London, England) · 2025Pooled it
- Eczema as a protective factor for brain cancer: a meta-analysis.BMC cancer · 2022Pooled it
- The role of diffusion-weighted MRI in biological image-guided radiation therapy: a roadmap.Physics in medicine and biology · 2026Review
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- TumorTwin: a Python framework for patient-specific digital twins in oncology.BMC medical informatics and decision making · 2026Article
- Predicting head and neck cancer response to radiotherapy using mathematical modeling of MRI-based habitats.NPJ precision oncology · 2026Article
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- Go-or-grow models in biology: a monster on a leash.Journal of mathematical biology · 2025Review
- Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review.Radiation oncology (London, England) · 2025Review
- 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
- Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.Seminars in radiation oncology · 2024Review
- A multifunctional targeted nano-delivery system with radiosensitization and immune activation in glioblastoma.Radiation oncology (London, England) · 2024Article
- Structural and practical identifiability of contrast transport models for DCE-MRI.PLoS computational biology · 2024Article
- Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes.Brain multiphysics · 2023Article
- Modelling microtube driven invasion of glioma.Journal of mathematical biology · 2023Article
- Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth.IEEE transactions on medical imaging · 2023Article
- A data assimilation framework to predict the response of glioma cells to radiation.Mathematical biosciences and engineering : MBE · 2023Article
- Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.Frontiers in artificial intelligence · 2023Article
- TME-targeted approaches of brain metastases and its clinical therapeutic evidence.Frontiers in immunology · 2023Review
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 1 country.
Funding
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Identifiers
What OpenQuestion holds
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.