发表机构
Institute of Pathology, Charité – Universitätsmedizin Berlin; Freie Universität Berlin; Humboldt-Universität zu Berlin; Berlin Institute of Health at Charité – Universitätsmedizin Berlin; Aignostics GmbH; Machine Learning Group, Technical University of Berlin; BIFOLD – Berlin Institute for the Foundations of Learning and Data; MVZ HPH Institut für Pathologie und Hämatopathologie GmbH（柏林夏里特医学院病理学研究所; 柏林自由大学; 柏林洪堡大学; 柏林夏里特医学院柏林卫生研究所; Aignostics有限公司; 柏林工业大学机器学习组; BIFOLD——柏林学习与数据基础研究所; HPH病理及血液病理诊断中心有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发并临床验证了LUCAID智能体多模态AI系统，其覆盖肺癌病理全流程任务，前瞻性验证中临床决策一致性达93.0%，优于经验丰富的胸病理学家，解决了现有AI工具的不足。
AI 中文摘要
肺癌组织诊断十分复杂，因为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。然而病理评估在很大程度上仍为视觉和半定量方式，且存在观察者间差异；现有人工智能（AI）工具仅覆盖部分任务，很少达到可泛化的专家级性能，且缺乏前瞻性临床验证。为应对这些挑战，我们开发并临床验证了LUCAID——一种用于肺癌精准病理学的智能体AI系统。该整合智能体将诊断推理与9个模块相结合，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、肿瘤细胞定量到预测生物标志物评分（PD-L1、MET、TROP-2）及自动结构化报告生成的完整常规工作流程。LUCAID允许用户交互式查询模块输出并生成能将结果情境化的报告。针对大规模专家真值标注，各分析模块的F1分数达0.82-0.95。在前瞻性临床验证中，LUCAID在临床可操作决策上与专家小组裁定的参考标准一致性达93.0%，而5名经验丰富的胸病理学家的一致性为68.3-81.1%。
英文摘要
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.
