ArticleJournal of translational medicine2023
An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Deep learning-assisted detection of lymph node metastases in bladder cancer.Diagnostic pathology · 2026Article
- Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer.Cancer science · 2026Article
- A Deep Learning-Generated Mixed Tumor-Stroma Ratio for Prognostic Stratification and Multi-omics Profiling in Bladder Cancer.Research (Washington, D.C.) · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review.Annals of surgical oncology · 2025Review
- Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.Cell death & disease · 2025Article
- Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer.BMC cancer · 2025Article
- Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN.Scientific reports · 2024Article
- Artificial Intelligence in Uropathology.Diagnostics (Basel, Switzerland) · 2024Review
- Lesion Localization and Pathological Diagnosis of Ovine Pulmonary Adenocarcinoma Based on MASK R-CNN.Animals : an open access journal from MDPI · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- A narrative review of advances in the management of urothelial cancer: Diagnostics and treatments.Bladder (San Francisco, Calif.) · 2024Review
- Preoperative fluorescence in situ hybridization analysis as a predictor of tumor recurrence in patients with non-muscle invasive bladder cancer: a bi-institutional study.Journal of translational medicine · 2023Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
- Machine Learning of Urine Cytology Highlights Increased Neutrophil Count in Muscle-Invasive Urothelial Carcinoma.Journal of cytologyArticle
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10 authors.
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Abstract
backgroundAccurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis.
methodsA total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value.
resultsThe AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability.
conclusionsWe reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.
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