ArticleJournal of translational medicine2021
Automated tumor proportion scoring for PD-L1 expression based on multistage ensemble strategy in non-small cell lung cancer.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Impact of PD-L1 Status on the Development of Cutaneous Immune-Related Adverse Events in Non-Small-Cell Lung Cancer Patients Receiving Immunotherapy.Life (Basel, Switzerland) · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms.Journal of clinical pathology · 2025Article
- PD-L1 Scoring Models for Non-Small Cell Lung Cancer in China: Current Status, AI-Assisted Solutions and Future Perspectives.Thoracic cancer · 2025Review
- A novel deep learning framework for automatic scoring of PD-L1 expression in non-small cell lung cancer.Biomolecules & biomedicine · 2025Article
- Pulsed electric field ablation as a candidate to enhance the anti-tumor immune response to immune checkpoint inhibitors.Cancer letters · 2025Review
- A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.Frontiers in immunology · 2025Article
- XCR1: A promising prognostic marker that pinpoints targeted and immune-based therapy in hepatocellular carcinoma.Heliyon · 2024Article
- Liquid biopsy for gastric cancer: Techniques, applications, and future directions.World journal of gastroenterology · 2024Review
- Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.NPJ precision oncology · 2024Article
- Interstitial lung diseases with concomitant lung cancer: a data mining approach revealing a complex condition with gender- and immune-associated specific implications.Frontiers in oncology · 2024Article
- Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer.Frontiers in immunology · 2022Article
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11 authors.
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Abstract
introductionProgrammed cell death ligand-1 (PD-L1) expression is a promising biomarker for identifying treatment related to non-small cell lung cancer (NSCLC). Automated image analysis served as an aided PD-L1 scoring tool for pathologists to reduce inter- and intrareader variability. We developed a novel automated tumor proportion scoring (TPS) algorithm, and evaluated the concordance of this image analysis algorithm with pathologist scores.
methodsWe included 230 NSCLC samples prepared and stained using the PD-L1(SP263) and PD-L1(22C3) antibodies separately. The scoring algorithm was based on regional segmentation and cellular detection. We used 30 PD-L1(SP263) slides for algorithm training and validation.
resultsOverall, 192 SP263 samples and 117 22C3 samples were amenable to image analysis scoring. Automated image analysis and pathologist scores were highly concordant [intraclass correlation coefficient (ICC) = 0.873 and 0.737]. Concordances at moderate and high cutoff values were better than at low cutoff values significantly. For SP263 and 22C3, the concordances in squamous cell carcinomas were better than adenocarcinomas (SP263 ICC = 0.884 vs 0.783; 22C3 ICC = 0.782 vs 0.500). In addition, our automated immune cell proportion scoring (IPS) scores achieved high positive correlation with the pathologists TPS scores.
conclusionsThe novel automated image analysis scoring algorithm permitted quantitative comparison with existing PD-L1 diagnostic assays and demonstrated effectiveness by combining cellular and regional information for image algorithm training. Meanwhile, the fact that concordances vary in different subtypes of NSCLC samples, which should be considered in algorithm development.
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