ArticleNature medicine2022
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.
Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 3 of them syntheses that pooled it.
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Who cites it
227 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Artificial intelligence for glaucoma.The Cochrane database of systematic reviews · 2025Pooled it
- Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.Frontiers in oncology · 2025Pooled it
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Validation of diagnostic artificial intelligence models for prostate pathology in a Middle Eastern cohort.Virchows Archiv : an international journal of pathology · 2026Article
- Morphology Predicts Grade, Transcriptomics Predicts Nodal Status: Task-Dependent Modality Contributions in Multimodal Prostate Cancer Classification.Journal of imaging · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Evaluating the Performance of LYDIA: An AI-Powered Assistant in the Detection of Metastatic Tumors to Optimize Clinical Workflows and Inform Soft Tissue Surgical Decision-Making.Bioengineering (Basel, Switzerland) · 2026Article
- Neighbor-constrained attention for multiple instance learning in whole-slide histopathology.Medical & biological engineering & computing · 2026Article
- XChondNet: Explainable Chondrogenic Tumor Diagnosis by Spatial-Context-Aware Synergistic Deep Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Deep learning augmented pathological grading and intraductal carcinoma of the prostate signatures improve recurrence prediction after intensity-modulated radiation therapy.Virchows Archiv : an international journal of pathology · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- nnMIL: a generalizable multiple instance learning framework for computational pathology.Nature biomedical engineering · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- 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
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Benefits of high-magnification and color information in computational pathology-An exploratory review.Journal of pathology informatics · 2026Review
- ISUP Grade Group Migration Between Prostate Biopsy and Radical Prostatectomy: A Systematic Review of Emerging Predictors.Biomedicines · 2026Review
167 more citing papers are in PubMed but not listed here.
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34 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.
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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.