<i>In Silico</i> Digital Twins of Bone Metastasis Enable Investigation of Tumor Progression and Therapy Response
The model offers a transformative tool for evaluating treatment strategies, including combination therapies, with the potential to accelerate the development of highly effective, targeted interventions for bone metastatic cancers. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
Authors
- Sergio Barrios (ORCID: https://orcid.org/0000-0003-0364-2466)
- Stefan Maksimovic (ORCID: https://orcid.org/0000-0001-7373-082X)
- Luca Marsilio (ORCID: https://orcid.org/0009-0001-9738-0438)
- Thomas Heyman (ORCID: https://orcid.org/0000-0002-5760-0176)
- Eleonora Dondossola (ORCID: https://orcid.org/0000-0001-6916-5418)
- Stefano Casarin (ORCID: https://orcid.org/0000-0002-7416-2366)
- Pietro Cerveri (ORCID: https://orcid.org/0000-0003-3995-8673)
- Elisa Serafini (ORCID: https://orcid.org/0009-0005-4070-1244)
- Michele Grimaldi (ORCID: https://orcid.org/0009-0004-8545-8607)
- Alice Maccarini (ORCID: https://orcid.org/0009-0008-2472-9687)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Houston Methodist (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of Pavia (IT)
- Methodist Hospital (US)
- La Rochelle Université (FR)
- Politecnico di Milano (IT)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1158/0008-5472.can-25-0088
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00