ArticleIEEE transactions on medical imaging2019
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
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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.
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Who cites it
75 citing papers in PubMed, 170 citations in OpenAlex.
- Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems.Nature communications · 2026Article
- PREDICT-GBM: A multicenter platform advancing personalized glioblastoma radiotherapy planning.NPJ digital medicine · 2026Article
- Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma patients.Physics in medicine and biology · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- TumorTwin: a Python framework for patient-specific digital twins in oncology.BMC medical informatics and decision making · 2026Article
- Article
- Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Toward Digital Twins for Optimal Radioembolization.PET clinics · 2026Review
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
- Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth.Cancers · 2025Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- 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
- Optimal Fractionation Scheduling for Radiotherapy Treatments with Reinforcement Learning, Tumor Growth Modeling and Outcome Modeling.Biomedicines · 2025Article
- Prognostic assessment of osteolytic lesions and mechanical properties of bones bearing breast cancer using neural network and finite element analysisMechanobiology in medicine · 2025Article
- Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology.ArXiv · 2025Article
- 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
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 7 institutions in 5 countries.
Funding
Abstract
Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.
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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.