ArticleCancers2021
Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Automatic framework for PD-L1 expression evaluation in Latino patients with non-small cell lung cancer.Journal of pathology informatics · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.Scientific reports · 2025Article
- PD-L1 expression assessment in Angiosarcoma improves with artificial intelligence support.Journal of pathology informatics · 2025Article
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- A graph-learning based model for automatic diagnosis of Sjögren's syndrome on digital pathological images: a multicentre cohort study.Journal of translational medicine · 2024Article
- Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.NPJ precision oncology · 2024Article
- Digital Pathology Applications for PD-L1 Scoring in Head and Neck Squamous Cell Carcinoma: A Challenging Series.Journal of clinical medicine · 2024Article
- Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.Cancer research communications · 2024Article
- Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review.Biomolecules & biomedicine · 2024Review
- Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in the Pathology of Gastric Cancer.Journal of gastric cancer · 2023Review
- The tumor ecosystem in head and neck squamous cell carcinoma and advances in ecotherapy.Molecular cancer · 2023Review
- PD-L1 testing by immunohistochemistry in immuno-oncology.Biomolecules & biomedicine · 2023Review
- Multimodality treatment in recurrent/metastatic squamous cell carcinoma of head and neck: current therapy, challenges, and future perspectives.Frontiers in oncology · 2023Review
- Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.Nature communications · 2022Article
- The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy.Cancers · 2022Review
- Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.21st century pathology · 2022Article
- From rough to precise: PD-L1 evaluation for predicting the efficacy of PD-1/PD-L1 blockades.Frontiers in immunology · 2022Review
- Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.