A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer Article Swipe
Jiao Feng
,
Zhanxian Shang
,
Hongmin Lu
,
Peilin Chen
,
Shiting Chen
,
Jianbo Xiao
,
Fuchuang Zhang
,
Dadong Zhang
,
Chunxin Lv
,
Yedan Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1540087
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1540087
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2025.1540087
- https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1540087/pdf
- OA Status
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- Cited By
- 2
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408990372Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2025.1540087Digital Object Identifier
- Title
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A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancerWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-31Full publication date if available
- Authors
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Jiao Feng, Zhanxian Shang, Hongmin Lu, Peilin Chen, Shiting Chen, Jianbo Xiao, Fuchuang Zhang, Dadong Zhang, Chunxin Lv, Yedan ChenList of authors in order
- Landing page
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https://doi.org/10.3389/fimmu.2025.1540087Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1540087/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1540087/pdfDirect OA link when available
- Concepts
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Medicine, Atezolizumab, Intraclass correlation, Lung cancer, Artificial intelligence, Clinical trial, Immunotherapy, Machine learning, Cancer, Medical physics, Computer science, Oncology, Nivolumab, Internal medicine, Clinical psychology, PsychometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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