eess.IVMay 25, 2026

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

Authors: Zhengrui Guo, Zhengyu Zhang, Jiabo Ma, Yihui Wang, Fengtao Zhou, Yingxue Xu, Ling Liang, Chenglong Zhao, +18 more

Organizations: Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China · 4Guangdong Provincial Key Laboratory of Molecular Tumor Pathology, Guangzhou, China · Department of Pathology, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China · Department of Pathology, The Affiliated Qingyuan Hospital (Qingyuan People’s Hospital), Guangzhou Medical University, Qingyuan, China · Department of Pathology, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou, China · 8HaploX Biotechnology, Shenzhen, China · Department of Pathology, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China · 10State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Pathology, School of Basic Medicine and Xijing Hospital, Fourth Military Medical University, Xi’an, China · Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China · Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China · Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China · 14Jinfeng Laboratory, Chongqing, China · Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China · 16Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China · 17State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong SAR, China · 18HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, The Hong Kong University of Science and Technology, Futian, Shenzhen, China

Abstract

Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evaluated across clinical workflows or prospectively validated in real-world settings. We introduce PulmoFoundation, a multi-center, prospectively validated, randomized controlled trial (RCT)-evaluated foundation model for comprehensive lung pathology assessment across pre-operative, intra-operative, and post-operative care. Built upon Virchow2 via subspecialty-specific pretraining using ~40,000 diagnostic H&E-stained whole-slide images (WSIs), PulmoFoundation was systematically evaluated on ~26,000 WSIs across 32 clinically relevant tasks. In addition to accurately predicting molecular markers and patient survival, our model achieves clinical-grade performance in core diagnostic tasks across biopsy, frozen section, and surgical resection slides. In a registered prospective study of 1,357 patients across 11 diagnostic tasks, our model achieved an average AUC of 92.3%. Using pre-specified triage thresholds, PulmoFoundation could reduce additional second-review burden for 68.8% of biopsies and 83.0% of frozen sections, and defer 44.5% of IHC stain orders, with PPVs of 1.0, 0.991, and 0.966. Beyond prospective validation, we conducted a crossover RCT with eight pathologists, in which AI assistance improved diagnostic accuracy across 4,928 case-reader pairs (91.7% w/ AI vs. 83.8% w/o AI). AI assistance also reduced median diagnostic time by 19.6%, increased diagnostic confidence by 8.7%, and improved inter-rater agreement from moderate (kappa = 0.56) to substantial (kappa = 0.76). Together, these evaluations support PulmoFoundation as a clinically validated decision-support system for lung pathology.

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