Bladder Cancer
Conditions
Brief summary
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."
Detailed description
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.
Interventions
None listed
Sponsors
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
* (1) Patients diagnosed with or suspected of having bladder cancer; (2) Patients with benign urological diseases, including ureteral calculi, bladder calculi, cystitis, and ureteral stricture; (3) Consent to the use of experimental urine specimens and tissue specimens; (4) Aged 18 to 75 years; (5) Willing to sign informed consent, comply with the protocol, demonstrate good adherence, and cooperate with follow-up visits.
Exclusion criteria
* (1) History of other malignant tumors within 5 years or prior receipt of any anti-cancer treatment; (2) Urothelial carcinoma coexisting with other genitourinary tumors (e.g., renal cell carcinoma or prostate cancer); (3) Non-standard procedures for sample collection or storage; (4) Incomplete relevant information of the subject; (5) Patients with insufficient communication, comprehension, or cooperation, or poor compliance, who cannot guarantee completion of follow-up as required; (6) Subjects deemed by the investigator to be unsuitable for participation in this clinical trial for any other reasons, leading to withdrawal from the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Discrimination of the diagnostic model | 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. | 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. |
Countries
China
Contacts
CONTACTXiongjun Ye
STUDY_CHAIRXiongjun Ye
+86 13910380916
Outcome results
None listed