ReviewFrontiers in oncology2022
Deep learning in bladder cancer imaging: A review.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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
16 citing papers in PubMed.
- AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.European urology oncology · 2026Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Diagnostic and Prognostic Non-Invasive Markers in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification.Diseases (Basel, Switzerland) · 2026Article
- Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study.Military Medical Research · 2026Article
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- Assessment of T1 and T2 Relaxation-Time Changes in NMIBC Tissue After 5-ALA Photodynamic Therapy Using Quantitative Magnetic Resonance Imaging.Biomedicines · 2025Article
- Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.Scientific reports · 2025Article
- Comparative Analysis of CT and MRI Combined with RNA Sequencing for Radiogenomic Staging of Bladder Cancer.International journal of molecular sciences · 2025Article
- Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Rapid identification of medicinal plants via visual feature-based deep learning.Plant methods · 2024Article
- Review
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.
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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.