ArticleScientific reports2018
Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Safety and immunogenicity of novel 5T4 viral vectored vaccination regimens in early stage prostate cancer: a phase I clinical trial.Journal for immunotherapy of cancer · 2020Trial
- Automatic framework for PD-L1 expression evaluation in Latino patients with non-small cell lung cancer.Journal of pathology informatics · 2026Article
- A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images.Scientific reports · 2026Article
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- PD-L1/PD-1 Expression in the Treatment of Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders: An Overview of Reviews.Journal of personalized medicine · 2025Review
- A novel deep learning framework for automatic scoring of PD-L1 expression in non-small cell lung cancer.Biomolecules & biomedicine · 2025Article
- Generating and evaluating synthetic data in digital pathology through diffusion models.Scientific reports · 2024Article
- Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.International journal of molecular sciences · 2024Review
- Review
- Immune Biomarkers in Triple-Negative Breast Cancer: Improving the Predictivity of Current Testing Methods.Journal of personalized medicine · 2023Review
- The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board.Pathologica · 2023Review
- Immunodiagnosis - the promise of personalized immunotherapy.Frontiers in immunology · 2023Review
- Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.Nature communications · 2022Article
- Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Application of Artificial Intelligence in Lung Cancer.Cancers · 2022Review
- Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.Histopathology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.
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