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

NCT07376057 | Not Yet Recruiting

• 1 other identifier: SYSKY-2025-940-01
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Prostatic Neoplasms, Castration-Resistant

Keywords

artificial intelligence; time to castration-resistant; prostate cancer; whole slide image

Outcome Measures

Primary Outcomes (1)

• C-index (Concordance Index)

  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 Outcomes (2)

• sensitivity

  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

  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.

Study Arms (1)

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)

Interventions

Artificial intelligence (AI)-based predictive model (developed) OTHER

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.

Patients undergo prostate biopsy

Eligibility Criteria

• Age: 18 Years+
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510120, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Histopathological slides of formalin-fixed, paraffin-embedded tissues from patients with prostate cancer undergoing prostate biopsy

MeSH Terms

Conditions

Prostatic Neoplasms, Castration-Resistant; Prostatic Neoplasms

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Shaoxu Wu, Ph.D

  Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University

  STUDY DIRECTOR

Central Study Contacts

Tianxin Lin, Ph.D

CONTACT

13724008338; lintx@mail.sysu.edu.cn

Shaoxu Wu, MD

CONTACT

15017581087; wushx29@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 21, 2026
• First Posted: January 29, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: January 29, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)