ArticlePeerJ2019
Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Article
- Targeting inflammation in cancer therapy: from mechanistic insights to emerging therapeutic approaches.Journal of translational medicine · 2025Review
- Inflammation in cancer: therapeutic opportunities from new insights.Molecular cancer · 2025Review
- Article
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry.Journal of personalized medicine · 2024Review
- Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.Frontiers in immunology · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Review
- See Lung Cancer with an AI.Cancers · 2023Review
- Deep Learning Approaches in Histopathology.Cancers · 2022Review
- PEG2-Induced Pyroptosis Regulates the Expression of HMGB1 and Promotes hEM15A Migration in Endometriosis.International journal of molecular sciences · 2022Article
- Application of Artificial Intelligence in Lung Cancer.Cancers · 2022Review
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- A narrative review of digital pathology and artificial intelligence: focusing on lung cancer.Translational lung cancer research · 2020Review
- Emerging role of deep learning-based artificial intelligence in tumor pathology.Cancer communications (London, England) · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy.
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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.