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.

翻译：罕见妇科肿瘤（RGTs）因其低发病率和高异质性带来了重大的临床挑战。缺乏明确的诊疗指南导致治疗管理欠佳且预后不良。分子肿瘤委员会通过超越癌症类型、依据生物标志物定制治疗方案，加速了有效疗法的获取。然而，需要人工整理的非结构化数据阻碍了生物标志物分析在疗法匹配中的高效运用。本研究探索利用大型语言模型（LLMs）构建用于RGTs精准医疗的数字孪生系统。我们的概念验证数字孪生系统整合了来自机构及已发表病例（n=21）的临床与生物标志物数据，以及文献数据（n=655篇文献，涉及n=404,265名患者），为转移性子宫癌肉瘤制定定制化治疗方案，识别出传统单一来源分析可能遗漏的治疗选项。LLM赋能的数字孪生能够高效模拟个体患者的疾病轨迹。从基于器官的肿瘤定义转向基于生物学的定义，有助于实现个性化诊疗，从而推动RGTs的管理并改善患者预后。