Proposal for a New Vesical Imaging-Reporting and Data System (VI-RADS)-Based Algorithm for the Management of Bladder Cancer: A Paradigm Shift From the Current Transurethral Resection of Bladder Tumor (TURBT)-Dependent Practice.
Magnetic resonance imaging
Muscle invasion
Radical cystectomy
Staging
Urothelial carcinoma
Journal
Clinical genitourinary cancer
ISSN: 1938-0682
Titre abrégé: Clin Genitourin Cancer
Pays: United States
ID NLM: 101260955
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
29
01
2022
revised:
27
02
2022
accepted:
01
03
2022
pubmed:
30
3
2022
medline:
27
7
2022
entrez:
29
3
2022
Statut:
ppublish
Résumé
Transurethral resection of bladder tumor (TURBT) is the essential first step in the current algorithm for the management of bladder cancer (BC). However, despite its necessity and significance, TURBT has several limitations, including cost, hospitalization, anesthesia, potential complications such as bladder perforation, and delay to radical cystectomy. The Vesical Imaging Reporting and Data System (VI-RADS) was developed to standardize the reporting of multiparametric magnetic resonance imaging for BC, and its diagnostic accuracy to predict muscle invasion has been validated. Given the high sensitivity of VI-RADS ≥ 3 and high specificity of VI-RADS ≥ 4 as clinically relevant cutoff values, we herein propose a new VI-RADS-based algorithm for the management of BC. Using this algorithm, patients with VI-RADS ≤ 2 may not need to undergo sampling of the detrusor muscle nor second TURBT even if there is no muscle in the initial TURBT specimen, whereas patients with VI-RADS ≥ 4 may skip conventional TURBT aimed at pathologic confirmation of muscle invasion and immediately undergo radical cystectomy. Our newly proposed algorithm enables the avoidance of unnecessary deep resection or second TURBT as well as delay to radical cystectomy. The VI-RADS-based algorithm enables a paradigm shift from the current TURBT-dependent practice in the management of BC.
Identifiants
pubmed: 35346591
pii: S1558-7673(22)00047-7
doi: 10.1016/j.clgc.2022.03.002
pii:
doi:
Banques de données
ISRCTN
['ISRCTN35296862']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e291-e295Informations de copyright
Copyright © 2022. Published by Elsevier Inc.