Fluorescence cystoscopy: the end of biopsies for carcinoma <i>in situ</i> detection?

Witjes, J. Alfred

doi:10.1111/bju.13018

BJU International

2015

DOI: 10.1111/bju.13018

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Fluorescence cystoscopy: the end of biopsies for carcinoma in situ detection?

J. Alfred Witjes

Abstract: Conflict of InterestJ.A.W. has been an investigator for Photocure and has been an advisor for and has received honoraria from Photocure and Ipsen.

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2021

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An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

Xiong

Pan

et al. 2021

JNCI: Journal of the National Cancer Institute

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Background Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. Methods In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. Results The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974–0.979) in the internal validation set and 0.990 (95% CI = 0.979–0.996), 0.982 (95% CI = 0.974–0.988), 0.978 (95% CI = 0.959–0.989), and 0.991 (95% CI = 0.987–0.994) in different external validation sets. In the CAIDS versus urologists’ comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902–0.964; and sensitivity = 0.954, 95% CI = 0.902–0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. Conclusions The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

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An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

Xiong

Pan

et al. 2021

JNCI: Journal of the National Cancer Institute

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Fluorescence cystoscopy: the end of biopsies for carcinoma in situ detection?

Abstract: Conflict of InterestJ.A.W. has been an investigator for Photocure and has been an advisor for and has received honoraria from Photocure and Ipsen.

Cited by 1 publication

References 6 publications

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

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