Development and validation of a predictive model for the diagnosis of bladder tumors using narrow band imaging.

Abstract

At present, the prediction of bladder tumor nature during cystoscopy is partially dependent on the clinician's own experience. Subjective factors may lead to excessive biopsy or delayed treatment. The purpose of our study is to establish a reliable model for predicting the nature of bladder tumors using narrow band imaging. From November 2021 to November 2022, the clinical data of 231 patients who required a cystoscopy were prospectively collected at our center. Cystoscopy was performed in 219 eligible patients, in which both tumor and vascular morphology characteristics were recorded. Pathological results were used as the diagnostic standard. A logistic regression analysis was used to screen out factors related to tumor pathology. Bootstrap resampling was used for internal validation. A total of 71 patients from four other centers served as an external validation cohort. The following diagnostic factors were identified: tumor morphology (cauliflower-like or algae-like lesions), vascular morphology (dotted or circumferential vessels), tumor boundary (clear or unclear), and patients' symptoms (gross hematuria) and were included in the prediction model. The internal validation results showed that the area under the curve was 0.94 (95% CI 0.92-0.97), and the P value from the goodness-of-fit test was 0.97. After external validation, the results showed the area under the curve was 0.89 (95% CI 0.82-0.97) and the P value of the goodness-of-fit test was 0.24. A diagnostic prediction nomogram was established for bladder cancer. The verification results showed that the prediction model has good prediction performance.