Journal Article Epublish
PubMed: 40230846 DOI: 10.3389/fimmu.2025.1540087
A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.
Front Immunol 16:1540087
Authors: Feng Jiao, Zhanxian Shang, Hongmin Lu, Peilin Chen, Shiting Chen, Jiayi Xiao, Fuchuang Zhang, Dadong Zhang, Chunxin Lv, Yuchen Han
Affiliations
- Department of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Department of Pathology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
- Department of Clinical and Translational Medicine, 3D Medicines Inc. Shanghai, China.
- School of Life Science and Technology, Tongji University, Shanghai, China.
- Department of Oncology, Shanghai Punan Hospital of Pudong New District, Shanghai, China.
Abstract
The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to identify patient populations likely to respond to ICI treatments, particularly in lung cancer treatment. Currently, the tumor proportion score (TPS), a crucial biomarker for patient selection, relies on manual interpretation by pathologists, which often shows substantial variability and inconsistency. To address these challenges, we innovatively developed multi-instance learning for TPS (MiLT), an innovative artificial intelligence (AI)-powered tool that predicts TPS from whole slide images. Our approach leverages multiple instance learning (MIL), which significantly reduces the need for labor-intensive cell-level annotations while maintaining high accuracy. In comprehensive validation studies, MiLT demonstrated remarkable consistency with pathologist assessments (intraclass correlation coefficient = 0.960, 95% confidence interval = 0.950-0.971) and robust performance across both internal and external cohorts. This tool not only standardizes TPS evaluation but also adapts to various clinical standards and provides time-efficient predictions, potentially transforming routine pathological practice. By offering a reliable, AI-assisted solution, MiLT could significantly improve patient selection for immunotherapy and reduce inter-observer variability among pathologists. These promising results warrant further exploration in prospective clinical trials and suggest new possibilities for integrating advanced AI in pathological diagnostics. MiLT represents a significant step toward more precise and efficient cancer immunotherapy decision-making.
MeSH
Lung, Pathologists, Neoplasms, Multiple Chronic Conditions, Immunotherapy, Lung Neoplasms, Deep Learning, Therapeutics, Learning, Artificial Intelligence