ArticleCancers2023
Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Human biomarker navigator.iMeta · 2026Review
- Machine learning-based multi-class classification of bladder pathologies using fused 3D CT radiomic and 3D auto-encoder deep features.European journal of radiology open · 2026Article
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer.Cancer science · 2026Article
- Artificial intelligence-assisted diagnosis and histopathological grading of bladder cancer: current status, challenges, and future directions.Frontiers in digital health · 2026Review
- Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?Abdominal radiology (New York) · 2025Review
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
- Accurate bladder cancer diagnosis using ensemble deep leaning.Scientific reports · 2025Article
- New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Uropathology.Diagnostics (Basel, Switzerland) · 2024Review
- The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction.Insights into imaging · 2024Review
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.Cancers · 2024Review
- Article
- Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.Frontiers in oncology · 2024Article
- The classification of the bladder cancer based on Vision Transformers (ViT).Scientific reports · 2023Article
- Imaging in Upper Tract Urothelial Carcinoma: A Review.Cancers · 2023Review
- Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate clinical staging of bladder cancer aids in optimizing the process of clinical decision-making, thereby tailoring the effective treatment and management of patients. While several radiomics approaches have been developed to facilitate the process of clinical diagnosis and staging of bladder cancer using grayscale computed tomography (CT) scans, the performances of these models have been low, with little validation and no clear consensus on specific imaging signatures. We propose a hybrid framework comprising pre-trained deep neural networks for feature extraction, in combination with statistical machine learning techniques for classification, which is capable of performing the following classification tasks: (1) bladder cancer tissue vs. normal tissue, (2) muscle-invasive bladder cancer (MIBC) vs. non-muscle-invasive bladder cancer (NMIBC), and (3) post-treatment changes (PTC) vs. MIBC.
Indexed as
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What OpenQuestion holds
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.