Ex vivo validation of a real-time multispectral endoscopic system for the detection and biopsy of bladder tumors.
Transl Androl Urol
; 10(6): 2373-2383, 2021 Jun.
Article
em En
| MEDLINE
| ID: mdl-34295724
ABSTRACT
BACKGROUND:
Real-time multispectral imaging (rMSI) simultaneously provides white light (WL), photodynamic diagnosis (PDD) images, and a real-time fusion of both. It may improve the detection of bladder tumors. However, rMSI has not been used for transurethral biopsy or resection so far. The aim of this ex vivo study was to test the feasibility of bladder tumor biopsies using the rMSI system and compare it to a conventional endoscopic system.METHODS:
A 3D printed rigid bladder phantom was equipped with small and flat (5 mm × 1 mm) mock-bladder-tumors made of silicone and fluorescent Qdots655 (Thermo Fisher Scientific, Germany). Urologists (n=15) were asked to perform a rigid cystoscopy and biopsy of all identified lesions (n=6) using a prototype rMSI system and the Image1 S system (Karl Storz, Tuttlingen). Success rate and completion time were measured. The image quality of both systems and the usability of the rMSI system according to the system usability scale (SUS) were evaluated with a task-specific questionnaire.RESULTS:
Tumor detection and biopsy rate were 100% (90/90) for the rMSI system and 98.9% (89/90) for the Image1 S system (P=0.3). The biopsy completion time did not differ significantly between the systems (P=0.48). Differentiation between healthy and suspect mucosa with the rMSI system was rated as comparable to the Image1 S system by 53% of surgeons and as better by 33% of the surgeons. The median SUS score for the rMSI system was 87.5%.CONCLUSIONS:
Accurate transurethral biopsies are feasible with the rMSI system. Furthermore, the rMSI system has an excellent SUS. This study paves the way to the first in-human transurethral resections of bladder tumors (TUR-B) using rMSI technology.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article