LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Organizations: Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Germany · Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH · Charité Digital Clinician · Aignostics Scientist Program,GmbH, Berlin,CharitéplatzGermany1, 10117 Berlin, Germany · Machine Learning Group, Technical University of Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany · MVZ HPH Institut für Pathologie und Hämatopathologie GmbH, Hamburg, Germany · German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Berlin Partner Site, Heidelberg, Germany · Institute of Pathology, Ludwig-Maximilians-University, Munich, Germany · Evangelische Lungenklinik Berlin-Buch, Berlin, Germany · Institute of Pathology, University Hospital Cologne, Cologne, Germany · Department of Mathematics and Computer Science, Technical University of Berlin, Germany · Department of Artificial Intelligence, Korea University, Seoul 136-713, South Korea · MPI for Informatics, Saarbrücken, Germany · German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich Partner Site, Heidelberg, Germany · Bavarian Cancer Research Center (BZKF), Munich Partner Site, Munich, Germany
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.