ArticleScientific reports2025
Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study.
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 10 papers, 3 of them syntheses that pooled it.
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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
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics for Prediction of Microsatellite Instability Status in Gastric Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- 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
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- Added value of synthetic MRI in refining biparametric MRI VI-RADS for predicting muscle-invasive bladder cancer.Abdominal radiology (New York) · 2026Article
- 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
- Review
- A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.NPJ digital medicine · 2026Article
- Comparative Analysis of CT and MRI Combined with RNA Sequencing for Radiogenomic Staging of Bladder Cancer.International journal of molecular sciences · 2025Article
- Integrated Magnetic Resonance Imaging in Muscle-Invasive Bladder Cancer: A Comprehensive Review.Cureus · 2025Review
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Authors and funding
19 authors.
Funding
Abstract
To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized to differentiate between MIBC and NMIBC based on three-channel image inputs, including original T2WI images, segmented bladder, and regions of interest. Inception V3 was employed for model construction. The accuracy, sensitivity (SN), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) for predicting MIBC by DL model were 92.4%, 94.7%, 91.5%, 81.8% and 97.7% in the validation set and 92.1%, 86.8%, 94.6%, 88.5% and 93.8% in the internal test set. In the external test set, these values were 81.6%, 57.1%, 87.1%, 50.0% and 90.0%. Additionally, the accuracy, SN, SP, PPV, and NPV for predicting MIBC were 93.5%, 100%, 93.4%, 11.1%, and 100% in VI-RADS 2; 80.0%, 66.7%, 87.2%, 73.7% and 82.9% in VI-RADS 3; 90.3%, 91.7%, 85.7%, 95.7%, 75.0% in VI-RADS 4. The accuracy, SN, and PPV were 93.9%, 93.9%, and 100% in VI-RADS 5. The DL model based on T2WI can effectively predict MIBC and serve as a valuable complement to VI-RADS 3.
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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.