ArticlePLOS digital health2026
From static prediction to dynamic cancer trajectories: Virtual Human Twins for breast cancer decision support.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
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Abstract
Digital oncology has become increasingly sophisticated at predicting cancer risk, treatment response, and prognosis. Yet many tools still operate as static snapshots, while cancer care unfolds as a trajectory shaped by tumor biology, genetics, treatment, residual disease, toxicity, and patient priorities. We argue that oncology needs dynamic, multiscale Virtual Human Twins (VHTs) that represent the patient and disease as they evolve together, integrating multimodal data to support longitudinal clinical reasoning and decision simulation rather than prediction alone. High-risk triple-negative breast cancer provides a focused first use case because trajectories are heterogeneous, decisions are time-sensitive, and clinical utility can be tested in tumor-board workflows. A minimum viable VHT should make uncertainty visible, support clinically interpretable simulation of alternative management strategies, and be judged by clinical decision impact, not predictive performance alone.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.