Digital Twins in Neuro-Oncology: A Systematic Review of Current Implementations, Technical Strategies, and Clinical Applications
Abstract
Purpose To perform a systematic review evaluating current digital twin (DT) implementations, highlighting clinical relevance and technical strategies, and identifying opportunities to advance personalized, predictive care in neuro-oncology. Materials and Methods PubMed, Scopus, and Web of Science databases were systematically screened for English-language original research articles published from inception through June 2025 focused on DT development, validation, or patient-specific computational models in neuro-oncology. Extracted variables included computational frameworks, data sources, clinical or predictive tasks, and reported outcomes. Risk of bias and applicability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), which revealed well-defined predictors and outcomes but frequent concerns regarding participants and analysis. Results Of the 73 articles reviewed, 21 met eligibility criteria. DTs simulated tumor growth, radiation response, immune interactions, and drug transport.Most models (n = 20) relied on mechanistic or biophysical frameworks, with increasing adoption of artificial intelligence-driven and hybrid approaches. A total of 12 studies focused on glioblastomas or high-grade gliomas, and 17 relied primarily on MRI data. Tumor-growth and treatment-response simulations were the most common DT applications. Only six studies provided publicly available code, and closed-loop calibration was reported in eight studies. Predictive…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 20 participants or records.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.