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Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images
Open accessAbstract
Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples. Materials and Methods: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone. Results: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P<< 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67-0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63-0.77, P ≤ 0.03). Conclusions: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.
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“While numerous deep learning studies have emerged for predicting ICI responses in NSCLC from H&E images, they are primarily focused on refining PD-L1 quantification56–58.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [57]
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“Numerous studies have demonstrated the potential for using AI to predict immunotherapy biomarkers from H&E images of patients with NSCLC, including tumor mutation burden (TMB; ref. 76), PD-L1 expression (105), and TILs (106, 107).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [105]
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Metadata
- Authors
- Sha, L., Osinski, B. L., Ho, I. Y., Tan, T. L., Willis, C., Weiss, H., Beaubier, N., Mahon, B. M., Taxter, T. J., & Yip, S. S. F.
- Affiliations
- Tempus Labs (United States) (US)
- Northwestern University (US)
- Corresponding author
- Yip, Stephen S. F
- Venue
- Journal of Pathology Informatics
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 10
- Issue
- 1
- Pages
- 24
- ISSN
- 2153-3539 (ISSN-L)
- PMC
- PMC6669997
- OpenAlex
- W2963652908
- Cited by
- 130
- References
- 48
- Citations by year
- 2026: 9 · 2025: 21 · 2024: 25 · 2023: 13 · 2022: 27 · 2021: 16 · 2020: 18 · 2019: 1
- FWCI
- 7.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Pathology Informatics (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- non-small cell lung cancer, PD-L1 expression, whole slide images, H&E staining, deep learning, ResNet, tumor microenvironment, immunohistochemistry, PD-1/PD-L1 inhibitors, histopathological features
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