LUCAID用智能体整合九项病理流程，提升肺癌诊断准确率与一致性。
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
- 构建智能体系统，融合九个模块覆盖病理全流程
- 临床验证中对关键决策的吻合率达93.0%，远超病理医生
- 适合临床病理医生、研究者及医学AI开发者参考
肺癌组织诊断复杂，精准肿瘤学中的治疗决策依赖于组织形态学、免疫组化和分子特征的整合。然而，病理评估仍以视觉和半定量为主，存在观察者间差异；现有AI工具仅覆盖部分任务，很少达到可泛化的专家水平，且缺乏前瞻性临床验证。为此，我们开发并临床验证了LUCAID——一种用于精准肺癌病理的智能体AI系统。一个集成智能体将诊断推理与九个模块结合，覆盖从质量控制、肿瘤检测与分割、组织学分型、肿瘤微环境分析、肿瘤细胞密度量化、预测性生物标志物评分（PD-L1、MET、TROP-2）到自动生成结构化报告的完整常规流程。用户可交互查询各模块输出，并生成上下文化报告。在大规模专家标注数据上，分析模块的F1分数达0.82–0.95。在前瞻性临床验证中，LUCAID在关键临床决策上的吻合率达93.0%，显著高于五位经验丰富的胸外科病理医生的68.3%–81.1%。
原文摘要 · Abstract (English)
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological assessment remains largely visual and semi-quantitative and shows interobserver variability, while existing artificial intelligence (AI) tools cover only selected tasks, rarely reach generalizable expert-level performance, and lack prospective clinical validation. To address these challenges, we developed and clinically validated LUCAID, an agentic AI system for precision lung cancer pathology. An integrative agent couples diagnostic reasoning with nine modules that cover the full routine workflow, from quality control, tumor detection and segmentation, histological subtyping, tumor microenvironment profiling, tumor cellularity quantification, and predictive biomarker scoring (PD-L1, MET, TROP-2) to automated structured report generation. LUCAID enables users to interactively query the module outputs and generate reports that contextualize the results. Against large-scale expert ground-truth annotations, the analysis modules achieved F1 scores of 0.82-0.95. In prospective clinical validation, LUCAID reached 93.0% concordance with an expert-panel adjudicated reference standard across clinically actionable decisions, compared with 68.3-81.1% for five experienced thoracic pathologists.
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