Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors Article Swipe
Jacqueline Lammert
,
Nicole Pfarr
,
Leonid Kuligin
,
Sonja Mathes
,
Tobias Dreyer
,
Luise Modersohn
,
Patrick Metzger
,
Dyke Ferber
,
Jakob Nikolas Kather
,
Daniel Truhn
,
Lisa C. Adams
,
Keno K. Bressem
,
Sebastian Lange
,
Kristina Schwamborn
,
Martin Boeker
,
Marion Kiechle
,
Ulrich A. Schatz
,
Holger Bronger
,
Maximilian Tschochohei
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.00544
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.00544
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.00544
- https://arxiv.org/pdf/2409.00544
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402953710Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.00544Digital Object Identifier
- Title
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Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological TumorsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-31Full publication date if available
- Authors
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Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin, Sonja Mathes, Tobias Dreyer, Luise Modersohn, Patrick Metzger, Dyke Ferber, Jakob Nikolas Kather, Daniel Truhn, Lisa C. Adams, Keno K. Bressem, Sebastian Lange, Kristina Schwamborn, Martin Boeker, Marion Kiechle, Ulrich A. Schatz, Holger Bronger, Maximilian TschochoheiList of authors in order
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https://arxiv.org/abs/2409.00544Publisher landing page
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https://arxiv.org/pdf/2409.00544Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2409.00544Direct OA link when available
- Concepts
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Medicine, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.proof-of-concept | 79 |
| abstract_inverted_index.literature-derived | 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 19 |
| citation_normalized_percentile |