Observational studyScientific reports2018
Automated Gleason grading of prostate cancer tissue microarrays via deep learning.
Observational study in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 148 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
148 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading.Prostate cancer and prostatic diseases · 2023Pooled it
- Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.The Journal of pathology · 2019Guideline
- Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.Scientific data · 2026Article
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.Virchows Archiv : an international journal of pathology · 2026Article
- Machine Learning Classification of Prostate Cancer Genomic Sequences Using K-Mer and Sequence-Derived Features.Computational molecular bioscience · 2026Article
- Investigation of angiogenesis and epithelial-mesenchymal transition markers (CD31, VEGF, and E-Cadherin) in prostatic cancer patients: a multi-omics study.World journal of surgical oncology · 2026Article
- Patient Perspectives on AI-Powered Medical Robots in Breast and Prostate Cancer Care: Qualitative Study.JMIR cancer · 2026Article
- Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.PloS one · 2026Article
- PANDA-PLUS: Improved dataset of prostate whole slide images from PANDA Challenge with pixel-level expert annotations.Journal of pathology informatics · 2026Article
- Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions.Nature communications · 2025Article
- A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images.Prostate cancer and prostatic diseases · 2025Article
- Automating Prostate Cancer Grading: A Novel Deep Learning Framework for Automatic Prostate Cancer Grade Assessment using Classification and Segmentation.Journal of imaging informatics in medicine · 2025Article
- Simultaneous classification and delineation of seven types of histological growth patterns in lung adenocarcinomas using self-supervised learning and online hard patch mining.Quantitative imaging in medicine and surgery · 2025Article
- Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.Scientific reports · 2025Article
- Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer.Nature communications · 2025Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Leveraging Machine Learning for Severity Level-Wise Biomarker Identification in Prostate Cancer Microarray Gene Expression Data.Biomedicines · 2025Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
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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.