Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors

TL;DR

This paper demonstrates how large language models can create digital twins that integrate diverse data sources to improve personalized treatment planning for rare gynecological tumors, addressing current clinical challenges.

Contribution

It introduces a novel LLM-enabled digital twin system that combines clinical, biomarker, and literature data for tailored therapy in RGTs, advancing precision medicine.

Findings

Digital twins identified treatment options missed by traditional analysis

Efficient modeling of individual patient trajectories

Potential to improve RGT management and patient outcomes

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 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.