Multiparametric cystoscopy: is the future here yet?

Ahmadi, Hamed; Daneshmand, Siamak

doi:10.21037/tau-20-1012

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Besides the assumption that MP imaging technology can increase the detection rate of bladder tumors, it is presumed that it can lead to a more complete TUR (20). Ahmadi and Daneshmand suggest that the greatest potential advantage of the rMSI system could be the minimization of the false positive rate and interobserver variability in image interpretation (22). So far, the rMSI system has only been used for cystoscopy and not during TUR of bladder tumors (TUR-B).…”

Section: Original Articlementioning

confidence: 99%

Ex vivo validation of a real-time multispectral endoscopic system for the detection and biopsy of bladder tumors

Grüne¹,

Rother²,

Waldbillig³

et al. 2021

Transl Androl Urol

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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) mockbladder-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.

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Section: Original Articlementioning

confidence: 99%

Ex vivo validation of a real-time multispectral endoscopic system for the detection and biopsy of bladder tumors

Grüne¹,

Rother²,

Waldbillig³

et al. 2021

Transl Androl Urol

View full text Add to dashboard Cite

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“…Bladder cancer is the sixth most common cancer in the United States and mostly identified as non-muscle invasive (NMIBC) [1]. Cystoscopy is a endoscopic procedure performed by urologists to visually inspect the inner lining of the bladder; it is commonly utilized as diagnostic tool to detect clinically suspicious lesions for bladder cancer [2]. Clinically suspicious lesions are treated by a surgical procedure called transurethral resection of bladder tumor (TURBT).…”

Section: Introductionmentioning

confidence: 99%

Potential of educational cystoscopy atlas for augmented intelligence

Eminaga

Laurie

Lee

et al. 2023

Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI

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Background: Cystoscopy is a common urological procedure for evaluation and treatment of diseases of the lower urinary tract. Cystoscopic recognition of cancerous and benign lesions of the bladder may be challenging given the wide spectrum of pathology and the experience of the urologist. Although computer-aided detection tools hold the potential to improve the urologist's performance, compiling a comprehensive dataset to train such tools remain a challenge. Educational cystoscopy atlas represents an alternative strategy to overcome this challenge. Materials and Methods:Mimicking the human behavior, we utilized an educational cystoscopy atlas to develop deep learning models for lesion detection. We applied a total of 312 images representing 7 major urologic findings from a cystoscopy atlas. A random image augmentation was applied to these images and included color contrast manipulation, image flipping and shearing. We utilized a neural architecture search to determine the optimal model design; For each case, we examined the area under the receiver operating characteristic (AUROC), the specificity, sensitivity, the positive predictive value (PPV) and the negative predictive value (NPV) and to classify frames according to the cancer presence status on 68 cystoscopy videos. Results:The median per-case AUROC for the frame classification was 0.680 by a median specificity of 0.312 and a median sensitivity of 0.867. At frame level, the median per-case PPV was 0.347 and the median per-case NPV was 0.837. All lesions were correctly flagged by our model. Conclusion:Our results unravel the potential utility of educational cystoscopy atlas for augmented intelligence.

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Efficient Augmented Intelligence Framework for Bladder Lesion Detection

Eminaga,

Lee,

Laurie

et al. 2023

JCO Clinical Cancer Informatics

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PURPOSE Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed. MATERIALS AND METHODS We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. RESULTS Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer. CONCLUSION Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.

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Multiparametric cystoscopy: is the future here yet?

Cited by 3 publications

References 25 publications

Ex vivo validation of a real-time multispectral endoscopic system for the detection and biopsy of bladder tumors

Ex vivo validation of a real-time multispectral endoscopic system for the detection and biopsy of bladder tumors

Potential of educational cystoscopy atlas for augmented intelligence

Efficient Augmented Intelligence Framework for Bladder Lesion Detection

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