Digital Twin models to address long-term treatment toxicities in children and young adults with cancer
Inas Elsayed, Aleksandar Krstic, Luis F. Iglesias-Martínez, Jessica C. Ralston, Walter Kolch · 2026
Read Digital Twin models to address long-term treatment toxicities in children and young adults with cancer by Inas Elsayed, Aleksandar Krstic, Luis F. Iglesias-Martínez, Jessica C. Ralston, Walter Kolch (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13357588.txt.
Description
Inas Elsayed and four co-authors review digital twin models for predicting late treatment harms in young cancer patients, in a paper from 2026. Modern therapy cures far more children, yet long-term toxicities rob survivors of healthy life. Early warning markers are essential for personal care that cuts those risks.
Digital twins are computer copies of a patient that update as data arrive and can forecast harm. The review explains how recent modeling advances make such twins feasible for late toxicity. It also sets out the hurdles for safe use in child cancer care, from data quality to clinical trust and workflow fit.
Catalog record
| CREATOR | Inas Elsayed, Aleksandar Krstic, Luis F. Iglesias-Martínez, Jessica C. Ralston, Walter Kolch |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |