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Hormuth II

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[2505.08927] Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology

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By Hormuth II
| arxiv.org
Work on one of the world's most important websites and make an impact on open science.
Open in Who Shared View Duplicates (548) Issue with article?

TumorTwin: A python framework for patient-specific digital twins in oncology

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By Hormuth II, Anirban Chaudhuri, Graham Pash, Rafael Bravo
| arxiv.org
[Submitted on 1 May 2025] Title:TumorTwin: A python framework for patient-specific digital twins in oncology View a PDF of the paper titled TumorTwin: A python framework for patient-specific digital twins in oncology, by Michael Kapteyn and 7 other authors View PDF HTML (experimental) Abstract:Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-specific basis.
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[2308.14925] Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

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By Hormuth II, Syed Rakin
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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