Exploration of Novel AI-enabled Blue Light Enhanced Cystoscopy
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Bladder Cancer, Non-muscle Invasive Bladder Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States, Norway
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
Blue light cystoscopy (BLC) is a diagnostic procedure in bladder cancer where the inside of the bladder is observed with a camera to detect bladder lesions. Unlike regular white light cystoscopy, blue light cystoscopy makes use of a drug that induces fluorescence under blue light preferentially in neoplastic and malignant cells that helps visualize bladder lesions during the cystoscopic procedure. Blue light cystoscopy has shown to improve detection of bladder cancer. Cystoscopy, including blue light cystoscopy, is a procedure involving assessment of the visual appearance of the bladder surface, leading to decisions of taking biopsies, remove suspicious areas and assign treatment options. The assessment is subjective and has a large operator variability. These shortcomings show an opportunity for computer aided detection (CADe) medical device to add value to both clinicians and patients. The objective of this data collection study is to build a high-quality, diverse data set of video, image recordings and relevant clinical data from BLC procedures performed as part of routine clinical practice to train a computer-aided detection (CADe) algorithm for real- time lesion detection during cystoscopy. The data will be used to support the training, non-clinical technical development and testing of such AI algorithms for use during cystoscopy and to provide documentation needed for training of such algorithms and to assist in guiding future validation of such algorithms. Exploratory purposes of the study is to use data to explore future AI algorithms in bladder cancer, such as computer-aided diagnosis (CADx) AI algorithms, image enhancement and cystoscopy improvement algorithms, including bladder mapping, tumor visualization, cystoscopy documentation, and combination models of image and clinical data including risk assessment, clinical outcomes, and disease modeling
Primary outcome measures
- Video and image collection [Time frame: 1 day]
Eligibility criteria
Inclusion criteria
- Age 18 or older
- Written informed consent, approved by relevant IRB/IEC, signed
- Hexvix/Cysview has been prescribed in the usual manner in accordance with the terms of the marketing authorization (see Appendix B)
- Physician has planned to do a blue light cystoscopy on the patient and to obtain biopsies, if clinically indicated, of suspicious lesions with video confirmation.
- Patient has not previously taken part in this study
Exclusion criteria
- None
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 3 centers
- Moffitt Cancer Center — Tampa
- Regents of the University of Michigan — Ann Arbor
- Rutgers Cancer Institute — New Brunswick
Norway · 1 center
- Oslo University Hospital — Oslo
Identifiers
NCT: NCT07144319 · PCAIX01/25