ArticleCommunications medicine2024
A comprehensive AI model development framework for consistent Gleason grading.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- 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
- Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping.Nature communications · 2026Article
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Adaptive diagnostic reasoning framework for pathology with multimodal large language models.Communications medicine · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
- Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Digital Pathology with AI for Cervical Biopsies: Diagnostic Accuracy at the CIN2+ Threshold.Cancers · 2025Article
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
- Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.BMC medical imaging · 2025Article
- StereoMM: a graph fusion model for integrating spatial transcriptomic data and pathological images.Briefings in bioinformatics · 2025Article
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Authors and funding
39 authors.
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Abstract
backgroundArtificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability.
methodsWe present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI.
resultsOur model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance.
conclusionsThis pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.
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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.