Prostatic Neoplasms, Castration-ResistantNCT07376057
Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer
An observational study of Prostatic Neoplasms, Castration-Resistant, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostatic Neoplasms, Castration-Resistant
- What is being tested?
- Artificial intelligence (AI)-based predictive model (developed)
- How many people
- 150 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2026to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Patients with other tumors and undergo systemic therapy .
- The patient refused to participate in this diagnostic test.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510120
Cuimei Yao, 13450210603, syskyk02@163.com
About this study
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.
Read the 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.
- Being tested
- Artificial intelligence (AI)-based predictive model (developed)
- Keywords
- artificial intelligence, time to castration-resistant, prostate cancer, whole slide image
- Medical subject headings
- Prostatic Neoplasms, Castration-Resistant, Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| Patients undergo prostate biopsy | Patients undergo prostate biopsy and are diagnosed with prostate cancer, who receive Hormone therapy. Receives: Other: Artificial intelligence (AI)-based predictive model (developed) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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
Measured: 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
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
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jan 2026
- Study start1 Jan 2026 (estimated)
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostatic Neoplasms, Castration-ResistantStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Shaoxu Wu, Ph.D, Study director, Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07376057
- Sponsor's protocol ID
- SYSKY-2025-940-01
- Status last verified
- Jan 2026
- First submitted
- 21 Jan 2026
- Last updated
- 29 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.
Study at a glance
From the registry record, last updated by the sponsor on 29 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Prospective Validation of a Pathology-Based Artificial Intelligence Model for Predicting the Time to Castration Resistance of Prostate Cancer
- ClinicalTrials.gov ID
- NCT07376057
- Acronym / sponsor ID
- SYSKY-2025-940-01
- Keywords
- artificial intelligence, time to castration-resistant, prostate cancer, whole slide image
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jan 2026 (estimated)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 29 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergo prostate biopsy | – | Other: Artificial intelligence (AI)-based predictive model (developed)Patients undergo prostate biopsy and are diagnosed with prostate cancer, who receive Hormone therapy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (2 listed)
- Patients with other tumors and undergo systemic therapy .
- The patient refused to participate in this diagnostic test.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Other | Patients undergo prostate biopsy |
Primary outcome
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.
2 secondary outcomes
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jan 2026.
- Individual participant dataSharing planned: No. To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.
Related trials for Prostatic Neoplasms, Castration-Resistant
RecruitingPhase 2
The COSMYC Trial (COmbined Suppression of MYC)
Metastatic Castration-resistant Prostate Cancer
RecruitingPhase 1/2
Docetaxel and SX-682 in Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma, Salivary Gland Carcinoma, and Advanced Prostate Cancer
Head and Neck Cancer, Head and Neck Squamous Cell Carcinoma
RecruitingObservational study
Pluvicto Real-world Investigation in Survival in Metastatic CRPC
Prostatic Neoplasms, Castration-Resistant, Neoplasm Metastasis
Active, not recruitingPhase 3
A Study to Learn About the Investigational Medicine Called PF-06821497 (Mevrometostat) in Men With mCRPC Who Were Previously Treated With Abiraterone Acetate for Prostate Cancer (MEVPRO-1).
Metastatic Castrate Resistant Prostate Cancer (mCRPC)
RecruitingPhase 1
A Study of [225Ac]Ac-AKY-2519 in Patients With Metastatic Castration-Resistant Prostate Cancer
Castration Resistant Metastatic Prostate Cancer, Prostate Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Shaoxu Wu, Ph.D study director
Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostatic Neoplasms, Castration-Resistant, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 29 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Tianxin Lin, Ph.D (13724008338, lintx@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jan 2026.