基于大语言模型的数字孪生在罕见妇科肿瘤精准医学中的应用
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
AI总结：
本研究利用大语言模型构建数字孪生，用于罕见妇科肿瘤的精准医学治疗方案设计，通过整合多源数据实现个性化治疗。
摘要：
罕见妇科肿瘤（RGTs）由于其低发病率和异质性，给临床带来重大挑战。缺乏明确的指南导致管理不善和预后差。分子肿瘤板通过根据生物标志物定制治疗，超越了癌症类型。需要手动整理的非结构化数据阻碍了生物标志物分析在治疗匹配中的有效使用。本研究探讨了使用大语言模型（LLMs）构建数字孪生用于RGTs精准医学的应用。我们的概念验证数字孪生系统整合了机构和发表病例的临床和生物标志物数据（n=21）和文献衍生数据（n=655篇出版物，包含n=404,265名患者），以创建针对转移性子宫 carcinosarcoma 的定制治疗方案，识别可能被传统单一来源分析遗漏的选项。LLM驱动的数字孪生能高效建模个体患者轨迹。转向基于生物学而非器官的肿瘤定义，能够实现个性化护理，从而推动RGT管理并改善患者预后。
英文摘要：
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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