LUCAID推進肺癌病理

Computer Science > Computer Vision and Pattern Recognition arXiv:2608.

23803 (cs) [Submitted on 24 Aug 2026] Title:LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology Authors:Marie-Lisa Eich, Kai Standvoss, Timo Milbich, Alexander Möllers, Miriam Hägele, Philipp Anders, Lars Tharun, Hanna Kontradiuk, Sebastian Kons, Nader Aldoj, Recepcan Adigüzel, Adam Narai, Lukas Hönig, Jonathan Striebel, Binru Yang, Mihnea P.

Dragomir, Marvin Sextro, Philipp Keyl, Philipp Jurmeister, Rosemarie Krupar, Evelyn Ramberger, James Wells, Julika Ribbat-Idel, Andreas Kunft, Hussam Shuaib, Christian Grohé, Reinhard Büttner, David Horst, Klaus-Robert Müller, Lukas Ruff, Maximilian Alber, Frederick Klauschen, Simon Schallenberg View a PDF of the paper titled LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology, by Marie-Lisa Eich and 32 other authors View PDF HTML (experimental) 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.Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.

LG) Cite as: arXiv:2608.23803 [cs.CV] (or arXiv:2608.23803v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.

23803 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Timo Milbich [view email] [v1] Mon, 24 Aug 2026 20:07:04 UTC (33,887 KB) Full-text links: Access Paper: View a PDF of the paper titled LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology, by Marie-Lisa Eich and 32 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

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