SynthesisDiagnostic cytopathology2022
Program death ligand-1 immunocytochemistry in lung cancer cytological samples: A systematic review.
Synthesis in Diagnostic cytopathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Program death ligand-1 immunocytochemistry in lung cancer cytological samples: A systematic review.Diagnostic cytopathology · 2022Pooled it
- Utility of Fine Needle Aspiration Supernatant for Real-Time PCR-Based Mutation Analysis in Non-Small Cell Lung Cancer.Diagnostic cytopathology · 2026Article
- Ability of lactylation-related genes to stratify patients with ovarian cancer and predict immunotherapy response.Translational cancer research · 2026Article
- Efficacy of cross-line anti-programmed death 1/programmed cell death-ligand 1 antibody in the treatment of advanced nonsmall cell lung cancer: A retrospective study.Health science reports · 2024Article
- Programmed death ligand-1 expression and occult lymph node metastasis in non-small cell lung cancer.Thoracic cancer · 2023Article
- Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network.Frontiers in immunology · 2023Article
- Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy.Biomedicines · 2022Review
- Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this era of personalized medicine, targeted immunotherapies like immune checkpoint inhibitors (ICI) blocking the programmed death-1 (PD-1)/program death ligand-1 (PD-L1) axis have become an integral part of treating advanced stage non-small cell lung carcinoma (NSCLC) and many other cancer types. Multiple monoclonal antibodies are available commercially to detect PD-L1 expression in tumor cells by immunohistochemistry (IHC). As most clinical trials initially required tumor biopsy for PD-L1 detection by IHC, many of the currently available PD-1/PD-L1 assays have been developed and validated on formalin fixed tissue specimens. The majority (>50%) of lung cancer cases do not have a surgical biopsy or resection specimen available for ancillary testing and instead must rely primarily on fine needle aspiration biopsy specimens for diagnosis, staging and ancillary tests. Review of the literature shows multiple studies exploring the feasibility of PD-L1 IHC on cytological samples. In addition, there are studies addressing various aspects of IHC validation on cytology preparations including pre-analytical (e.g., different fixatives), analytical (e.g., antibody clone, staining platforms, inter and intra-observer agreement, cytology-histology concordance) and post-analytical (e.g., clinical outcome) issues. Although promising results in this field have emerged utilizing cytology samples, many important questions still need to be addressed. This review summarizes the literature of PD-L1 IHC in lung cytology specimens and provides practical tips for optimizing analysis.
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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.