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Pictorial review of multiparametric MRI in bladder urothelial carcinoma with variant histology: pearls and pitfalls.

Authors :
Arita Y
Woo S
Kwee TC
Shigeta K
Ueda R
Nalavenkata S
Edo H
Miyai K
Das J
Andrieu PIC
Vargas HA
Source :
Abdominal radiology (New York) [Abdom Radiol (NY)] 2024 Aug; Vol. 49 (8), pp. 2797-2811. Date of Electronic Publication: 2024 Jun 07.
Publication Year :
2024

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.<br /> (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Details

Language :
English
ISSN :
2366-0058
Volume :
49
Issue :
8
Database :
MEDLINE
Journal :
Abdominal radiology (New York)
Publication Type :
Academic Journal
Accession number :
38847848
Full Text :
https://doi.org/10.1007/s00261-024-04397-3