This study will, for the first time, establish a non-invasive diagnostic model for bladder cancer based on CREPT expression levels in exfoliated urothelial cells using a multicenter cohort, and validate its value in the non-invasive diagnosis and recurrence monitoring of bladder cancer. Its high sensitivity is expected to overcome the technical limitations of existing biomarkers and provide an innovative solution for establishing an integrated precision diagnosis and treatment system encompassing "screening, diagnosis, and monitoring."
The main research content of this project will be carried out from the following aspects: (1) Establish an exploratory cohort comprising patients with bladder cancer, patients with benign urological diseases, and healthy individuals. The exploratory cohort will be randomly divided into a training cohort and an internal validation cohort in a 2:1 ratio. Using the training cohort, a non-invasive detection method for bladder cancer based on CREPT expression in exfoliated urothelial cells will be developed, and a non-invasive diagnostic model for bladder cancer based on CREPT expression in exfoliated urothelial cells will be constructed. The diagnostic performance of the model will then be validated using the internal validation cohort; (2) Establish an external validation cohort including patients with bladder cancer, benign urological diseases, and healthy individuals to evaluate the clinical diagnostic performance of the model; (3) Enroll patients with bladder cancer undergoing follow-up cystoscopy after Transurethral Resection of Bladder Tumor (TURBT) and patients requiring repeat TURBT to establish a recurrence monitoring cohort, and evaluate the performance of this diagnostic model in predicting bladder cancer recurrence.
Study Type
OBSERVATIONAL
Enrollment
250
National Cancer Center / Cancer Hospital, Chinese Academy of Medical Sciences
Beijing, Chaoyang District, China
RECRUITINGDiscrimination of the diagnostic model
In the internal validation of the exploratory cohort and the external validation cohort, calculate the AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the diagnostic model, using "pathological diagnosis results" as the gold standard, to validate the model's ability to distinguish between "bladder cancer" and "non-bladder cancer (benign diseases + healthy individuals)". Among these, an AUC ≥ 0.85 in the external validation cohort is the primary indicator.
Time frame: Short-term follow-up for 6 months, with follow-ups conducted at 3 months and 6 months post-enrollment; long-term follow-up for 1 year, with an additional follow-up at 12 months post-enrollment.
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