ArticleHistopathology2025
Comparative performance of PD-L1 scoring by pathologists and AI algorithms.
Article in Histopathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 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
- CLDN18.2 expression identifies a gastric-type phenotype and a potential therapeutic target in pulmonary invasive mucinous adenocarcinoma.Diagnostic pathology · 2026Article
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- Spatial omics and AI for clinically actionable cancer biomarkers.PLoS medicine · 2026Article
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Endometrial immune dysregulation shapes CD8Frontiers in immunology · 2026Article
- Deep learning-based assessment of PD-L1 expression in NSCLC predicts outcome for patients treated with anti-PD-1 immunotherapy.Frontiers in immunology · 2026Article
- Clinical relevance of AI-based PD-L1 scoring in non-small cell lung cancer.Frontiers in oncology · 2026Article
- AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.Scientific reports · 2025Article
- Next-Generation Sequencing: A Review of Its Transformative Impact on Cancer Diagnosis, Treatment, and Resistance Management.Diagnostics (Basel, Switzerland) · 2025Review
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Artificial Intelligence (AI) for Programmed Death Ligand-1 (PD-L1) Immunohistochemical Assessment in Urothelial Carcinomas: "Teaching" Cell Differentiation to AI Systems.Life (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
aimThis study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD-L1 expression in non-small cell lung carcinoma (NSCLC). Immune-checkpoint inhibitors have revolutionized NSCLC treatment, with PD-L1 expression, measured as the tumour proportion score (TPS), serving as a critical predictive biomarker for therapeutic response. METHODS AND
resultsIn our analysis, 51 SP263-stained NSCLC cases were scored by six pathologists using light microscopy and whole-slide images (WSI), alongside evaluations by two commercially available software tools: uPath software (Roche) and the PD-L1 Lung Cancer TME application (Visiopharm). The study examined intra- and interobserver agreement among pathologists at TPS cutoffs of 1% and 50%, revealing moderate interobserver agreement (Fleiss' kappa 0.558) for TPS <1% and almost perfect agreement (Fleiss' kappa 0.873) for TPS ≥50%. Intraobserver consistency was high, with Cohen's kappa ranging from 0.726 to 1.0. Comparisons between the AI algorithms and the median pathologist scores showed fair agreement for uPath (Fleiss' kappa 0.354) and substantial agreement for the Visiopharm application (Fleiss' kappa 0.672) at the 50% TPS cutoff.
conclusionThese results indicate that while there is strong interobserver concordance among pathologists at higher TPS levels, the performance of AI algorithms is less consistent. The study underscores the need for further refinement of AI tools to match the reliability of expert human evaluation, particularly in critical clinical decision-making contexts.
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