From static prediction to dynamic cancer trajectories: Virtual Human Twins for breast cancer decision support.

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Bibliographic Details
Title: From static prediction to dynamic cancer trajectories: Virtual Human Twins for breast cancer decision support.
Authors: Tan YY; Department of Obstetrics and Gynecology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria., Janickova I; Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.; Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Vienna, Austria.; Christian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria., Langs G; Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.; Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna, Vienna, Austria.; Christian Doppler Laboratory for Machine Learning Driven Precision Imaging, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Source: PLOS digital health [PLOS Digit Health] 2026 Jul 09; Vol. 5 (7), pp. e0001548. Date of Electronic Publication: 2026 Jul 09 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2767-3170
DOI:10.1371/journal.pdig.0001548