Last reviewed · How we verify

NCT07376057

Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer

Not yet recruiting Last updated 29 January 2026
What this trial tests

trial testing Artificial intelligence (AI)-based predictive model (developed) in Prostatic Neoplasms, Castration-Resistant in 150 participants. Not yet recruiting.

Timeline
1 January 2026
Primary endpoint
31 December 2028
31 December 2028

Quick facts

Lead sponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University
StatusNot yet recruiting
Study typeOBSERVATIONAL
Enrollment150
Start date1 January 2026
Primary completion31 December 2028
Estimated completion31 December 2028
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Who can join

18 and older, male only, with Prostatic Neoplasms, Castration-Resistant. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The goal of this predictive test is to prospectively test the performance of pre-developed artificial intelligence (AI) predictive model for predicting the time to castration resistance of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

Verify or expand the search:

Other recruiting trials for Prostatic Neoplasms, Castration-Resistant

Currently open trials in the same condition.

Other Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07376057.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing