Problem
Research questions and friction points this paper is trying to address.
Lung Cancer
Precision Pathology
Interobserver Variability
Artificial Intelligence
Clinical Validation
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
🤖 AI Summary
为解决肺癌病理诊断复杂且现有AI工具性能有限的问题，开发了LUCAID系统，集成多种功能模块，实现精准诊断，并在临床验证中表现出高一致性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Multimodal AI
Precision Pathology
Automated Structured Report Generation
Clinical Validation
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