Comparison between spectra modes of STORZ professional image enhancement systemTM and white light cystoscopy in diagnosis of bladder lesions
Year: 2026
Type: article
Abstract: ABSTRACT Introduction: White light cystoscopy (WLC) often fails to detect flat or subtle bladder lesions. Enhanced imaging techniques offer improved detection. The Storz Professional Image Enhancement System (SPIES TM ) overcomes these limitations and enables concurrent display of WLC and enhanced images, potentially improving tumor detection. In this study, the SPIES TM system was integrated into the diagnostic cystoscopy protocol to compare Spectra TM modes with conventional WLC in the diagnosis of bladder lesions. Methods: This 1-year prospective observational pilot study included 150 patients with suspected or known bladder tumors. Each patient initially underwent WLC, followed by Spectra B TM and Spectra A TM modes. Lesions were mapped according to EAU guidelines; suspicious areas were biopsied or resected for histopathological analysis. Results: WLC showed a sensitivity of 96% and specificity of 45%, with a false positive rate (FPR) of 54.5%. After WLC, Spectra B did not detect any additional lesions, whereas Spectra A identified three additional lesions, including a T2 recurrence. Spectra A TM had 100% sensitivity, but lower specificity (35%) and higher FPR (65%). Spectra A TM detected more lesions than WLC, although the difference was not statistically significant ( P = 0.131). The increase in FPR of the Spectra A TM was significant ( P = 0.04). Conclusions: Spectra A TM showed higher sensitivity and identified additional cancerous lesions, including one muscle-invasive tumor missed by WLC. However, it also had a significantly higher FPR. While promising, it has its limitations. Spectra A TM shows potential and warrants further research in larger, multicenter trials.
Source: Indian Journal of Urology
Institution: Artemis Hospitals
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7127327170