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Large language models-enabled digital twins for precision medicine in rare gynecological tumors
Jacqueline Lammert1,2,3,4, Nicole Pfarr5,6, Leonid Kuligin7
1Department of Gynecology and Center for Hereditary Breast and Ovarian Cancer, Technical University of Munich (TUM), School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, Munich, Germany. Jacqueline.Lammert@tum.de.
Abstract:
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) 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.
Insights
Large language models (LLMs) create digital twins for rare gynecological tumors (RGTs), enabling personalized treatment plans. This approach improves therapy matching by integrating diverse data for better patient outcomes.
Area of Science:
- Oncology
- Computational Biology
- Genomics
Background:
- Rare gynecological tumors (RGTs) pose significant clinical challenges due to low incidence and heterogeneity.
- Current management often lacks clear guidelines, leading to suboptimal patient outcomes.
- Biomarker profiling is crucial for precision medicine but hindered by unstructured data.
Purpose of the Study:
- To explore the application of large language models (LLMs) in developing digital twins for precision medicine in RGTs.
- To demonstrate the utility of LLM-enabled digital twins in tailoring treatment plans for RGTs.
- To overcome data integration challenges for improved therapy matching.
Main Methods:
- Development of a proof-of-concept digital twin system using LLMs.
- Integration of clinical and biomarker data from institutional and published cases (n=21).
- Incorporation of literature-derived data from 655 publications for comprehensive analysis.
Main Results:
- The LLM-enabled digital twin system successfully integrated diverse datasets.
- Tailored treatment plans were generated for metastatic uterine carcinosarcoma.
- The system identified potential therapeutic options missed by traditional analysis.
Conclusions:
- LLM-enabled digital twins can efficiently model individual patient trajectories in RGTs.
- Shifting to a biology-based tumor definition facilitates personalized care.
- This approach holds promise for advancing RGT management and improving patient outcomes.
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