ArticleNPJ breast cancer2021
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies.
Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Predicability of PD-L1 expression in cancer cells based solely on H&E-stained sections.Journal of pathology informatics · 2025Review
- AI microscope facilitates accurate interpretation of HER2 immunohistochemical scores 0 and 1+ in invasive breast cancer.Scientific reports · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Synergistic H&E and IHC image analysis by AI predicts cancer biomarkers and survival outcomes in colorectal and breast cancer.Communications medicine · 2025Article
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Immunotherapy in breast cancer: current landscape and emerging trends.Experimental hematology & oncology · 2025Review
- Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine.European journal of breast health · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.Frontiers in immunology · 2025Article
- Unraveling the complexity of PD-L1 assays: a descriptive review of the methodology, scoring, and practical implications.Frontiers in oncology · 2025Review
- An algorithm-based technique for counting mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections.Journal of cancer research and clinical oncology · 2024Article
- Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.NPJ precision oncology · 2024Article
- Concordance of assessments of four PD-L1 immunohistochemical assays in esophageal squamous cell carcinoma (ESCC).Journal of cancer research and clinical oncology · 2024Article
- Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.Cancer research communications · 2024Article
- Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review.Biomolecules & biomedicine · 2024Review
- Breaking Barriers: AI's Influence on Pathology and Oncology in Resource-Scarce Medical Systems.Cancers · 2023Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods.Journal of personalized medicine · 2023Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936-0.962) from moderate in RS1 (0.674, 95% CI: 0.614-0.735) and RS2 (0.736, 95% CI: 0.683-0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953-0.964) and 13% (0.815, 95% CI: 0.803-0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% "fully accepted" and 91% "almost accepted". The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.
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