Blue Light Cystoscopy With Cysview® Registry

Data will be captured on specific patient types undergoing Blue Light Cystoscopy with Cysview for known or suspected non-muscle invasive bladder cancer. Specific clinical questions will be asked.

1. What is the incremental detection rate with Blue Light Cystoscopy with Cysview over conventional white light cystoscopy in each of the seven (7) patient populations? Does this translate into lower recurrence/progression rate? 2. How do the six (6) tumor variables used in the European Association of Urology (EAU) risk tables (primary/secondary, recurrence rate, size, multifocality, grade, and history of carcinoma in situ (CIS))6 affect this incremental rate? 3. How does an abnormal cytology or positive or negative fluorescent in situ hybridization (FISH) affect the likelihood that Blue Light Cystoscopy with Cysview will detect more cancers than white light? 4. What are the performance characteristics of Blue Light Cystoscopy with Cysview within eight (8) weeks of Bacillus Calmette-Guérin (BCG) with respect to improved tumor detection and false positive rate compared to conventional white light cystoscopy? 5. What is the incremental Blue Light Cystoscopy with Cysview detection rate over random bladder biopsies alone in patients being evaluated for routine three month restaging (group 4) or occult disease (group 5)? 6. What are the performance characteristics of Blue Light Cystoscopy with Cysview after repeated Blue Light Cystoscopy with Cysview evaluations with respect to improved tumor detection, false positive rate and safety compared to conventional white light? 7. Does an abnormal urinalysis help identify patients with inflammation more likely to have false positive Blue Light Cystoscopy with Cysview results? 8. What is the practical learning curve for becoming "proficient" with Blue Light Cystoscopy with Cysview? 9. What is the overall false positive rate with Blue Light Cystoscopy with Cysview? 10. Can Blue Light Cystoscopy with Cysview make the resection more complete? If yes, is this due to improved margins and/or additional tumors seen under blue light? 11. What are the performance characteristics of BLC™ with Cysview® on patients with variant histology? 12. How does BLC™ with Cysview® impact detection of tumors in surveillance and patient management decisions? 13. How does BLC™ with Cysview® impact management of small tumors during office biopsy/fulguration? 14. How can BLC impact patient selection for bladder cancer sparing drugs? 15. Does the combination of BLC with urine biomarkers guide appropriate therapy and or change management? 16. Does BL have an impact on long term outcomes such as recurrence or progression? 17. Can AI models be optimized and validated to accurately predict patient response to intravesical therapy (such as BCG or intravesical chemotherapy) in non-muscle invasive bladder cancer? 18. How concordant are the predictions of AI algorithms with treatment decisions documented in real-world clinical registries? 19. How do AI-predicted responses correlate with patient outcomes, including recurrence and progression, within predefined clinical subgroups such as 'BCG-naïve' or 'no prior intravesical treatment'? 20. What is the potential impact of implementing AI-based predictive models on changes in management (e.g., treatment selection, patient management, and overall care outcomes)?