ArticleCancers2020
PD-L1 Multiplex and Quantitative Image Analysis for Molecular Diagnostics.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Nuclear PD-L1: an emerging oncogenic driver and promising therapeutic target in cancer.Journal of biomedical science · 2026Review
- Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- Unlocking therapeutic potential in traumatic brain injury: exploring microenvironmental targets, signaling pathways and translational hurdles.Inflammopharmacology · 2025Review
- Closer proximity of pre-treatment CD4Translational lung cancer research · 2025Article
- Fluorescence Lifetime Imaging Enables In Vivo Quantification of PD-L1 Expression and Intertumoral Heterogeneity.Cancer research · 2025Article
- Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review.Biomolecules & biomedicine · 2024Review
- Article
- Technical note on the exploration of COVID-19 in autopsy material.Journal of clinical pathology · 2023Article
- Exploring the immune microenvironment in small bowel adenocarcinoma using digital image analysis.PloS one · 2023Article
- The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy.Cancers · 2022Review
- The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy.International journal of molecular sciences · 2022Review
- Biomarkers of response to checkpoint inhibitors beyond PD-L1 in lung cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- PD-L1 and beyond: Immuno-oncology in cytopathology.Cytopathology : official journal of the British Society for Clinical Cytology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Multiplex immunofluorescence (mIF) and digital image analysis (DIA) have transformed the ability to analyse multiple biomarkers. We aimed to validate a clinical workflow for quantifying PD-L1 in non-small cell lung cancer (NSCLC). NSCLC samples were stained with a validated mIF panel. Immunohistochemistry (IHC) was conducted and mIF slides were scanned on an Akoya Vectra Polaris. Scans underwent DIA using QuPath. Single channel immunofluorescence was concordant with single-plex IHC. DIA facilitated quantification of cell types expressing single or multiple phenotypic markers. Considerations for analysis included classifier accuracy, macrophage infiltration, spurious staining, threshold sensitivity by DIA, sensitivity of cell identification in the mIF. Alternative sequential detection of biomarkers by DIA potentially impacted final score. Strong concordance was observed between 3,3'-Diaminobenzidine (DAB) IHC slides and mIF slides (R
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Identifiers
What OpenQuestion holds
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.