Patient digital twins
A digital twin is a computer model of one patient's tumour and body, updated with each scan and blood test, used to forecast how the disease will respond to each option before it is tried.
Overview
Patient digital twins combine mechanistic models (growth, radiobiology, pharmacokinetics, immune dynamics) with a patient's own imaging, genomics and markers, re-fitted as new data arrive. Prototypes forecast glioma growth for radiotherapy planning, optimise radiation fractionation for individual patients, simulate CAR-T expansion and choose adaptive therapy thresholds; the US National Academies set out a research agenda for digital twins in 2023 and the FDA has begun to discuss their evidentiary standards. They differ from virtual control arms, which model a typical patient rather than this one, and they stand or fall on the identifiability of their parameters from sparse clinical data.
How it works
A patient-specific state model is calibrated and continually updated from that patient's data, then run forward under alternative treatments to compare predicted outcomes.
- Individual forecasts rather than averages
- Integrates every model on this page
- Natural framework for adaptive treatment
- Parameters often not identifiable from clinical data
- No regulatory pathway yet
- Validation requires prospective trials
Latest papers
topQuery for this technology: (TITLE:"Patient digital twins" OR ABSTRACT:"Patient digital twins") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Patient digital twins, not a curated reading list.
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