ArticleHeliyon2024
Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.The Journal of clinical endocrinology and metabolism · 2026Observational
- Current Status and Future Perspective for Bladder Cancer MR Imaging and the Vesical Imaging-Reporting and Data System (VI-RADS) in Japan: Challenges and Solutions.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.European radiology · 2026Article
- Review
- A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.NPJ digital medicine · 2026Article
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- Integrated Magnetic Resonance Imaging in Muscle-Invasive Bladder Cancer: A Comprehensive Review.Cureus · 2025Review
- A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.Scientific reports · 2025Article
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rationale and objectives: To develop and validate a deep learning (DL) model to automatically diagnose muscle-invasive bladder cancer (MIBC) on MRI with Vision Transformer (ViT). Materials and methods: This multicenter retrospective study included patients with BC who reported to two institutions between January 2016 and June 2020 (training dataset) and a third institution between May 2017 and May 2022 (test dataset). The diagnostic model for MIBC and the segmentation model for BC on MRI were developed using the training dataset with 5-fold cross-validation. ViT- and convolutional neural network (CNN)-based diagnostic models were developed and compared for diagnostic performance using the area under the curve (AUC). The performance of the diagnostic model with manual and auto-generated regions of interest (ROI Results: The training and test datasets included 170 and 53 patients, respectively. Mean AUC of the top 10 ViT-based models with 5-fold cross-validation outperformed those of the CNN-based models (0.831 ± 0.003 vs. 0.713 ± 0.007-0.812 ± 0.006, p < .001). The diagnostic model with ROI Conclusion: The DL model effectively diagnosed MIBC. The ViT-based model outperformed CNN-based models, highlighting its utility in medical image analysis.
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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.