Translational barriers to digital twins in radiation oncology

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Authors: Federico Mastroleo, Mariana Borras-Osorio, Shiv P. Patel, David M. Routman, Doug J. Moseley, Satomi Shiraishi, Anthony Foong, Mark R. Waddle
Cited by: 1
References: 26

Abstract

Digital twin research in radiation oncology has expanded rapidly across multiple domains, yet the field lacks definitional consensus and validated translational frameworks. A systematic search (PubMed, Scopus and Web of Science - September 2025) identified 903 records and six original studies met inclusion criteria. Appraisal of the available original studies revealed three recurring translational barriers: misuse of the term "digital twin" for virtual humans or patient-specific predictive models; overreliance on internal or in-silico validation; and limited benchmarking against clinically established alternatives. Progress toward clinical translation requires disciplined nomenclature, real-patient external validation, head-to-head benchmarking, explicit attention to data-pipeline and regulatory pathways.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Effects of Radiation Exposure Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
Physics and Imaging in Radiation Oncology
Publisher
Elsevier BV
Volume
40
Pages
101024–101024
Publication date
18 June 2026
Language
EN