用大模型构建肿瘤数字孪生，助力罕见妇科癌精准治疗
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
- 用大模型整合临床与分子数据，生成个性化治疗方案
- 基于655篇文献40万患者数据，发现传统方法遗漏的治疗选项
- 突破器官分类局限，推动罕见妇科癌个体化诊疗
罕见妇科肿瘤（RGTs）因发病率低且异质性强，临床管理困难，缺乏明确指南导致治疗效果不佳。分子肿瘤会诊通过生物标志物而非癌症类型定制治疗方案，可加速有效疗法获取。然而，非结构化数据需人工整理，阻碍了生物标志物分析与治疗匹配。本研究探索利用大语言模型（LLMs）构建用于罕见妇科肿瘤精准医学的数字孪生系统。该概念验证系统整合了机构及发表病例数据（n=21）和文献数据（n=655篇论文，涵盖n=404,265名患者），为转移性子宫肉瘤生成个性化治疗方案，识别出传统单源分析可能遗漏的潜在治疗选择。基于大模型的数字孪生可高效模拟个体患者病程。从以器官为基础转向以生物学特征为基础的肿瘤定义，有助于实现个性化医疗，有望改善罕见妇科肿瘤的管理与预后。
原文摘要 · Abstract (English)
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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