ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.World journal of surgical oncology · 2025Pooled it
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- AI-Assisted Scoring Improves Interobserver Agreement in Breast Cancer Biomarker Evaluation.Cancers · 2026Article
- Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.Life (Basel, Switzerland) · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
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- Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation.Virchows Archiv : an international journal of pathology · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- AI driven pre-regulatory validation of PD-L1 analysis in lung cancer.Scientific reports · 2025Article
- Predicability of PD-L1 expression in cancer cells based solely on H&E-stained sections.Journal of pathology informatics · 2025Review
- Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.Diagnostic pathology · 2025Article
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.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
Corrections and comments
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Authors and funding
19 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standardized programmed death-ligand 1 (PD-L1) assessment in non-small cell lung cancer (NSCLC) is challenging, owing to inter-observer variability among pathologists and the use of different antibodies. There is a strong demand for the development of an artificial intelligence (AI) system to obtain high-precision scores of PD-L1 expression in clinical diagnostic scenarios. We developed an AI system using whole slide images (WSIs) of the 22c3 assay to automatically assess the tumor proportion score (TPS) of PD-L1 expression based on a deep learning (DL) model of tumor detection. Tests were performed to show the diagnostic ability of the AI system in the 22c3 assay to assist pathologists and the reliability of the application in the SP263 assay. A robust high-performance DL model for automated tumor detection was devised with an accuracy and specificity of 0.9326 and 0.9641, respectively, and a concrete TPS value was obtained after tumor cell segmentation. The TPS comparison test in the 22c3 assay showed strong consistency between the TPS calculated with the AI system and trained pathologists (R = 0.9429-0.9458). AI-assisted diagnosis test confirmed that the repeatability and efficiency of untrained pathologists could be improved using the AI system. The Ventana PD-L1 (SP263) assay showed high consistency in TPS calculations between the AI system and pathologists (R = 0.9787). In conclusion, a high-precision AI system is proposed for the automated TPS assessment of PD-L1 expression in the 22c3 and SP263 assays in NSCLC. Our study also indicates the benefits of using an AI-assisted system to improve diagnostic repeatability and efficiency for pathologists.
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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.