ArticleJournal of pathology informatics2019
Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.
Article in Journal of pathology informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it, 124 citations in OpenAlex.
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Deep Learning-Based Tumor Cell Classification in Lung Adenocarcinoma With a Case-by-Case Human-in-the-Loop Approach.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Article
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy.Frontiers in immunology · 2026Article
- AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.International journal of molecular sciences · 2025Review
- Predicability of PD-L1 expression in cancer cells based solely on H&E-stained sections.Journal of pathology informatics · 2025Review
- Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.Diagnostic pathology · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research.Journal of thoracic disease · 2025Review
- Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction.Journal for immunotherapy of cancer · 2025Review
- Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer.Cancers · 2025Review
- Comparative performance of PD-L1 scoring by pathologists and AI algorithms.Histopathology · 2025Article
- The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM.Pathologica · 2025Article
- Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.Translational lung cancer research · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A novel deep learning framework for automatic scoring of PD-L1 expression in non-small cell lung cancer.Biomolecules & biomedicine · 2025Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor 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
methodsOne hundred and thirty NSCLC patients were randomly assigned to training (
resultsThe 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,
conclusionsA 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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