ArticleFrontiers in artificial intelligence2023
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas.
Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications.Radiology. Imaging cancer · 2026Pooled it
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Dynamic image-informed selection of biomechanical tumor growth models.Biomechanics and modeling in mechanobiology · 2026Article
- Construction Strategies, Microenvironmental Modelling and Precision-Therapy Applications of Glioma Organoid Models.Cancers · 2026Review
- Translational barriers to digital twins in radiation oncology.Physics and imaging in radiation oncology · 2026Article
- Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.Journal of Korean Neurosurgical Society · 2026Review
- An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.Medical & biological engineering & computing · 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
- Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.Breast (Edinburgh, Scotland) · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Role of health digital twins in oncology drug development - a primer.Frontiers in oncology · 2026Review
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Multi-scale digital twins for personalized medicine.Frontiers in digital health · 2026Review
- From digital twins to clinically trustworthy twins: a clinical-claim-based validation framework for personalized digital health.Frontiers in digital health · 2026Article
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives.International journal of pharmaceutics: X · 2025Review
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
- A Generative Patient Digital Twin for Sequential Treatment of Oropharyngeal Squamous Carcinomas.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an
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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.