FindTrial
Sign In

An artificial intelligence model for predicting molecular typing, tumor microenvironment phenotype, efficacy, and prognosis of bladder cancer based on multimodal data: a multicenter real-world cohort study

An artificial intelligence model for predicting molecular typing, tumor microenvironment phenotype, efficacy, and prognosis of bladder cancer based on multimodal data: a multicenter real-world cohort study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300070863
Enrollment
Unknown
Registered
2023-04-25
Start date
2023-05-01
Completion date
Unknown
Last updated
2023-06-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Bladder Cancer

Interventions

Group of bladder cancer:none

Sponsors

Department of Urology, Xiangya Hospital, Central South University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: Minimum enrollment age > 18 years Do not set a maximum age (represented by <100 years old) The patient is suspected of having bladder cancer or the patient is pathological diagnosed with bladder cancer.

Exclusion criteria

Exclusion criteria: NA

Design outcomes

Primary

MeasureTime frame
Imageomic features;—

Secondary

MeasureTime frame
Overall Survival;Disease-free survival;—

Countries

China

Contacts

Public ContactXiongbing Zu

Xiangya Hospital Central South University

zxbxyyy@126.com+86 13787157190

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026