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.
@article{arxiv.2409.00544,
title = {Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors},
author = {Jacqueline Lammert and Nicole Pfarr and Leonid Kuligin and Sonja Mathes and Tobias Dreyer and Luise Modersohn and Patrick Metzger and Dyke Ferber and Jakob Nikolas Kather and Daniel Truhn and Lisa Christine Adams and Keno Kyrill Bressem and Sebastian Lange and Kristina Schwamborn and Martin Boeker and Marion Kiechle and Ulrich A. Schatz and Holger Bronger and Maximilian Tschochohei},
journal= {arXiv preprint arXiv:2409.00544},
year = {2026}
}
Comments
20 pages, 2 figures, 3 tables, supplements, original article