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

January 21, 2026 updated by: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Hormone therapy is an important treatment method for prostate cancer and can effectively extend the survival of patients. However, almost all patients will progress to castration-resistant prostate cancer at different times. Current Hormone therapy options include androgen deprivation therapy(ADT), anti-androgen receptor(AR), and chemotherapy, with combination therapy being more effective in the early stages but associated with greater side effects. Therefore, predicting the time to castration-resistant progression and using this information to apply personalized treatment plans can ensure efficacy while reducing drug side effects. Therefore, we have developed an artificial intelligence predictive model for predicting the time to castration resistance of prostate cancer, which is expected to accurately predict the progression time for different patients and assist doctors in making personalized and precise treatment plans based on individual progression risks.

This study is a predictive test with no intervention measures, planning to collect pathological slides of prostate biopsy from the enrolled patients and digitise them into whole-slide images (WSIs). The AI model will analyse the WSIs and generate slide-level predictive results (within 12 months, between 12 to 24months or over 24 months). The routine therapy and examination will be performed as usual. These two processes will not interfere with each other. Then we will follow-up the patients for 24 months, to record the time to castration-resistant progression, then we will compare the results with predictive model.

Study Type

Observational

Enrollment (Estimated)

150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510120
        • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with prostate cancer, undergo prostate biopsy between Jan, 2026 and Dec 2026 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University are planned to be enrolled in this prospective predictive test. Histopathological slides of biopsy tissues of enrolled patients will be collected and digitised as whole-slide images (WSIs) for prospective validation of the AI model.

Description

Inclusion Criteria:

  1. Patients are diagnosed with intermediate- to high-risk prostate cancer; undergo prostate biopsy
  2. Patients only received endocrine therapy for prostate cancer;
  3. Patients with complete clinical and pathological information.
  4. Patients agree to participate in this diagnostic test.

Exclusion Criteria:

  1. Patients with other tumors and undergo systemic therapy .
  2. The patient refused to participate in this diagnostic test.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients undergo prostate biopsy
Patients undergo prostate biopsy and are diagnosed with prostate cancer, who receive Hormone therapy.
Other: Artificial intelligence (AI)-based predictive model (developed)
Collect pathological slides of prostate biopsy of the enrolled patients. Digitise these slides into whole-slide images (WSIs). Analyze the WSIs using the AI model to generate predictive results (within 12 months, between 12 to 24months or over 24 months). No intervention to patients would be performed in this predictive test study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
C-index (Concordance Index)
Time Frame: For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the C-index of the AI model will be evaluated through study completion, an average of 3 year.
The proportion of all patient pairs in which the predicted outcome order matches the actual outcome order. It estimates the probability that the predicted results are consistent with the observed outcomes.
For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the C-index of the AI model will be evaluated through study completion, an average of 3 year.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
sensitivity
Time Frame: For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.
The output of the predictive model is divided into a binary variable using a 12-month threshold: TTCR <12 months is considered a positive outcome, and TTCR ≥12 months is considered a negative outcome. Accordingly, patients with TTCR <12 months are positive patients, and those with TTCR ≥12 months are negative patients. The number of correctly predicted positive slides (TTCR<12 months), to be divided by the number of positive slides in total
For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.
specificity
Time Frame: For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the specificity of the AI model will be evaluated through study completion, an average of 3 year.
The output of the predictive model is divided into a binary variable using a 12-month threshold: TTCR <12 months is considered a positive outcome, and TTCR ≥12 months is considered a negative outcome. Accordingly, patients with TTCR <12 months are positive patients, and those with TTCR ≥12 months are negative patients. The number of correctly predicted negative slides (TTCR≥12 months), to be divided by the number of negative slides in total
For each enrolled patient, the predictive results of AI model will be obtained in not long after prostate biopsy, and the specificity of the AI model will be evaluated through study completion, an average of 3 year.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

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

Investigators

  • Study Director: Shaoxu Wu, Ph.D, Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

January 1, 2026

Primary Completion (Estimated)

December 31, 2028

Study Completion (Estimated)

December 31, 2028

Study Registration Dates

First Submitted

January 21, 2026

First Submitted That Met QC Criteria

January 21, 2026

First Posted (Actual)

January 29, 2026

Study Record Updates

Last Update Posted (Actual)

January 29, 2026

Last Update Submitted That Met QC Criteria

January 21, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SYSKY-2025-940-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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