ReviewAbdominal radiology (New York)2024
Pictorial review of multiparametric MRI in bladder urothelial carcinoma with variant histology: pearls and pitfalls.
Review in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detecting bladder carcinoma.World journal of urology · 2025Pooled it
- Added value of synthetic MRI in refining biparametric MRI VI-RADS for predicting muscle-invasive bladder cancer.Abdominal radiology (New York) · 2026Article
- AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application.BJR artificial intelligence · 2026Article
- A case of plasmacytoid urothelial carcinoma with characteristic radiological findings.Abdominal radiology (New York) · 2025Article
- Predicting variant histology in bladder cancer: the role of multiparametric MRI and vesical imaging-reporting and data system (VI-RADS).Abdominal radiology (New York) · 2025Article
- Multiparametric MRI Features of Plasmacytoid Urothelial Carcinoma of the Urinary Bladder.Korean journal of radiology · 2025Article
- Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Multiparametric MRI and artificial intelligence in predicting and monitoring treatment response in bladder cancer.Insights into imaging · 2025Article
- Use of Multiparametric and Biparametric Magnetic Resonance Imaging in Bladder Cancer Staging: Prospective Observational Study and Analysis of Radiologist Learning Curve.Journal of clinical medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
Bladder cancer (BC), predominantly comprising urothelial carcinomas (UCs), ranks as the tenth most common cancer worldwide. UCs with variant histology (variant UC), including squamous differentiation, glandular differentiation, plasmacytoid variant, micropapillary variant, sarcomatoid variant, and nested variant, accounting for 5-10% of cases, exhibit more aggressive and advanced tumor characteristics compared to pure UC. The Vesical Imaging-Reporting and Data System (VI-RADS), established in 2018, provides guidelines for the preoperative evaluation of muscle-invasive bladder cancer (MIBC) using multiparametric magnetic resonance imaging (mpMRI). This technique integrates T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE)-MRI, and diffusion-weighted imaging (DWI) to distinguish MIBC from non-muscle-invasive bladder cancer (NMIBC). VI-RADS has demonstrated high diagnostic performance in differentiating these two categories for pure UC. However, its accuracy in detecting muscle invasion in variant UCs is currently under investigation. These variant UCs are associated with a higher likelihood of disease recurrence and require precise preoperative assessment and immediate surgical intervention. This review highlights the potential value of mpMRI for different variant UCs and explores the clinical implications and prospects of VI-RADS in managing these patients, emphasizing the need for careful interpretation of mpMRI examinations including DCE-MRI, particularly given the heterogeneity and aggressive nature of variant UCs. Additionally, the review addresses the fundamental MRI reading procedures, discusses potential causes of diagnostic errors, and considers future directions in the use of artificial intelligence and radiomics to further optimize the bladder MRI protocol.
Indexed as
Identifiers
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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.