用AI自动标注放疗患者临床结局，提升效率与准确性。
RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale
- 构建自主LLM代理，自动提取病患信息并判断临床结果。
- 在头颈癌和前列腺癌队列中准确识别骨放射坏死与复发。
- 适合大规模放疗研究，提升临床数据标注速度与一致性。
人工标注限制了放射肿瘤学中患者结局研究的规模、准确性和及时性。我们提出RadOnc-GPT，一种基于大语言模型（LLM）的自主代理，可独立检索患者特定信息，迭代评估证据，并返回结构化结局标签。评估明确验证了RadOnc-GPT在两个逐步复杂度层级上的表现：(1) 结构化质量保证（QA）层级，评估人口统计与放疗计划细节的准确检索；(2) 复杂临床结局标注层级，涉及头颈癌患者下颌骨放射性坏死（ORN）的判定，以及独立前列腺癌和头颈癌队列中癌症复发的检测，需结合结构化与非结构化患者数据进行综合分析。QA层级建立了对结构化数据检索的信任基础，这是实现复杂临床结局标注的关键前提。
原文摘要 · Abstract (English)
Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based agent capable of independently retrieving patient-specific information, iteratively assessing evidence, and returning structured outcomes. Our evaluation explicitly validates RadOnc-GPT across two clearly defined tiers of increasing complexity: (1) a structured quality assurance (QA) tier, assessing the accurate retrieval of demographic and radiotherapy treatment plan details, followed by (2) a complex clinical outcomes labeling tier involving determination of mandibular osteoradionecrosis (ORN) in head-and-neck cancer patients and detection of cancer recurrence in independent prostate and head-and-neck cancer cohorts requiring combined interpretation of structured and unstructured patient data. The QA tier establishes foundational trust in structured-data retrieval, a critical prerequisite for successful complex clinical outcome labeling.
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