ArticleScientific reports2025
Multi-institutional validation of AI models for classifying urothelial neoplasms in digital pathology.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
3 citing papers in PubMed.
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Gene-Morphology Alignment via Graph-Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in Pancreatic Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a deep learning approach for classifying normal, noninvasive, and invasive urothelial neoplasms via digitized histopathologicalimages. Despite many artificial intelligence (AI) models for cancer diagnosis, few focus on bladder lesions or differentiate between these critical categories. We developed convolutional neural networks (CNNs) and transformer-based models, which were trained on 12,500 whole-slide images (WSIs) from five institutions, with preprocessing steps including stain normalization and patch extraction. Fivefold cross-validation was used for evaluation against expert-annotated labels. Among tested models, EfficientNet-B6 achieved the highest performance, with an accuracy of 0.913 (95% confidence interval (CI), 0.907-0.920), sensitivity of 0.909 (95% CI, 0.904-0.914), specificity of 0.956 (95% CI, 0.953-0.960), F1-score of 0.906 (95% CI, 0.901-0.911), and an area under the receiver operating characteristic curve (AUC) of 0.983 (95% CI, 0.982-0.984). These results demonstrate the effectiveness and generalizability of AI-based bladder cancer classification.
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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.