ArticleNPJ digital medicine2021
Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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
25 citing papers in PubMed, 1 synthesis or guideline 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
- An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.NPJ digital medicine · 2026Article
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
- Article
- Longitudinal outcome prediction of prostate cancer patients on active surveillance using multiple instance learning.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images.Scientific reports · 2025Article
- A deployment safety case for AI-assisted prostate cancer diagnosis.Computers in biology and medicine · 2025Article
- Article
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy.Scientific reports · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.NPJ precision oncology · 2023Article
- Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning.Medical & biological engineering & computing · 2023Article
- Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology.PloS one · 2023Article
- An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases.Patterns (New York, N.Y.) · 2022Article
- The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study.Patient safety in surgery · 2022Article
- Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.Nature communications · 2022Article
- Addressing Challenges and Controversies in the Management of Prostate Cancer with Multidisciplinary Teams.Targeted oncology · 2022Review
- Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3-82.7%), the Cohen's kappa score (κ) was 0.650 (95% CI: 0.570-0.730), and the quadratic-weighted kappa score (κ
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Registered trials
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.