ArticleNPJ precision oncology2023
An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading.
Article in NPJ precision oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 25 citations in OpenAlex.
- Validation of diagnostic artificial intelligence models for prostate pathology in a Middle Eastern cohort.Virchows Archiv : an international journal of pathology · 2026Article
- Precision Diagnostics in Prostate Cancer: Integrating Biomarkers, Imaging, Genomics, and Artificial Intelligence in Contemporary United States Practice.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Generalizability of a biopsy-trained artificial intelligence algorithm to TURP and HoLEP specimens for detection of incidental prostate adenocarcinoma.Diagnostic pathology · 2026Article
- Retrospective Validation of a Resource-Aware Assistive AI Tool for Screening Prostate Needle Biopsies and Classifying Prostate Adenocarcinoma into ISUP Grade Groups.Life (Basel, Switzerland) · 2026Article
- The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.Scientific data · 2026Article
- Diagnosis of Prostate Cancer: A Comparative Evaluation of Biological Techniques.Diseases (Basel, Switzerland) · 2026Review
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- Automating tumor-stroma ratio quantification in colon cancer patients from the UNITED study.ESMO open · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.Scientific reports · 2025Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Integrative Transcriptomic-Histological Analysis in Dilated Cardiomyopathy Unveils FGFR1 Inhibition as Anti-Cardiac Fibrotic and Cardioprotective Therapy.JACC. Basic to translational science · 2025Article
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
- Enhanced hierarchical attention mechanism for mixed MIL in automatic Gleason grading and scoring.Scientific reports · 2025Article
- The Role of Artificial Intelligence in the Evaluation of Prostate Pathology.Pathology international · 2025Review
- Comprehensive testing of large language models for extraction of structured data in pathology.Communications medicine · 2025Article
- The Use of Artificial Intelligence in Urologic Oncology: Current Insights and Challenges.Research and reports in urology · 2025Review
- Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms.Cell reports. Medicine · 2024Article
Corrections and comments
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Authors and funding
22 authors at 12 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.
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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.