ArticleVirchows Archiv : an international journal of pathology2019
Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.
Article in Virchows Archiv : an international journal of pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 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
- XChondNet: Explainable Chondrogenic Tumor Diagnosis by Spatial-Context-Aware Synergistic Deep Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
- 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
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma.Regenerative therapy · 2025Article
- Fractal dimensions for tumour-related cell types of prostate cancer on histopathology images using multiple-threshold box counting algorithm.Biophysics and physicobiology · 2025Article
- An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.Journal of medical systems · 2024Article
- Artificial Intelligence in Uropathology.Diagnostics (Basel, Switzerland) · 2024Review
- A comprehensive AI model development framework for consistent Gleason grading.Communications medicine · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Using multi-label ensemble CNN classifiers to mitigate labelling inconsistencies in patch-level Gleason grading.PloS one · 2024Article
- ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification.Computer methods and programs in biomedicine · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.Current urology reports · 2023Review
- Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer.Cancers · 2023Article
- Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology.PloS one · 2023Article
- Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architectures.PeerJ. Computer science · 2023Article
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
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8 authors.
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Abstract
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG.
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