Prostate Cancer (Diagnosis)NCT07690306
Development and Validation of a Non-Invasive AI Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT
An observational study of Prostate Cancer (Diagnosis), run by Xiangya Hospital of Central South University at 6 sites in 2 countries.
Trial summary
Prostate cancer is one of the most common malignancies in men. Currently, due to the limited diagnostic accuracy of existing imaging tests, there is a risk of missed diagnosis or unnecessary prostate biopsy. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Cancer (Diagnosis)
- How many people
- 1,500 participants
- Where
- 6 sitesin 2 countries
- Study dates
- 13 Jan 2026to 31 Dec 2026
- 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: This study will enroll patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at six major hospitals in China: Xiangya Hospital of Central South University, Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, Beijing Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine. The study population includes two groups: patients with pathologically confirmed prostate cancer (cases) and patients with pathologically confirmed benign prostatic hyperplasia (BPH, controls). All participants are male, aged 18 years or older, with no prior prostate cancer treatment (endocrine therapy or radiotherapy) or prostate surgery. Patients with severe renal insufficiency or other malignancies are excluded. A total of approximately 1,000 to 1,500 participants will be enrolled retrospectively from the participating centers.
Who may be able to join
- Age ≥ 18 years.
- ECOG performance status 0-2.
- Life expectancy > 6 months.
- Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export.
- Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard.
- Complete clinical data available, including pre-treatment PSA (tPSA, fPSA), TNM stage, Gleason score, PI-RADS score, SUVmax, prostate volume (from MRI/PSMA PET), age, and BMI.
- Informed consent for data use for research purposes according to each center's ethics requirements.
Who cannot join
- History of other malignant tumors
- Previous prostate surgery (e.g., TURP)
- Prior endocrine therapy or radiotherapy
- Severe renal insufficiency
- Major organ dysfunction or life expectancy < 1 year
Trial locations and contacts
6 sites in 2 countries. Contact the study team or a site near you to ask about taking part.
6 sites in 2 countries
China5 sites
Beijing Hospital
Beijing, Beijing Municipality, 100730
Chinese PLA General Hospital
Beijing, Beijing Municipality, 100853
The First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, 510120
Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Renji Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200127
United States1 site
David Geffen School of Medicine University of California, Los Angeles(UCLA)
Los Angeles, California, 90095
About this study
Official title: A Retrospective, Multicenter Study to Develop and Validate a Non-Invasive Artificial Intelligence Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT, With Pathologically Confirmed Diagnosis as the Reference Standard
Prostate cancer is one of the most common malignancies in men. Currently, due to the limited diagnostic accuracy of existing imaging tests, there is a risk of missed diagnosis or unnecessary prostate biopsy. This study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model using two advanced imaging techniques: multiparametric MRI (mpMRI) and PSMA PET/CT. By integrating information from both imaging modalities, the AI model is expected to improve the diagnostic accuracy of prostate cancer, reduce unnecessary biopsies, and assist physicians in making better clinical decisions. This is a retrospective, multicenter study that plans to collect imaging and pathology data from approximately 1,000 to 1,500 patients across six major hospitals in China. The diagnostic performance of the model will be evaluated, including its ability to identify clinically significant prostate cancer and its value in assisting diagnosis in patients with PSA levels in the gray zone (4-20 ng/mL).
Read the detailed description
Study Design:
This is a retrospective and prospective, multicenter, case-control study. The study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model for prostate cancer by integrating multiparametric MRI (mpMRI) and PSMA PET/CT imaging.
Participants:
Patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at participating centers will be retrospectively enrolled. Eligible participants include pathologically confirmed prostate cancer patients (cases) and benign prostatic hyperplasia (BPH) patients (controls). Inclusion criteria include age ≥18 years, ECOG performance status 0-2, life expectancy >6 months, and availability of complete clinical data (PSA, Gleason score, PI-RADS, SUVmax, prostate volume, etc.). Exclusion criteria include prior prostate cancer treatment (endocrine therapy or radiotherapy), previous prostate surgery (e.g., TURP), severe renal insufficiency, other malignancies. Informed consent is waived for retrospective patients, while signed informed consent is required for prospective patients.
Sample Size:
Approximately 1,000 to 1,500 participants will be enrolled from six hospitals in China: Xiangya Hospital of Central South University, Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, Beijing Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine.
AI Model Development:
The AI model will be developed using deep learning and radiomics techniques. mpMRI sequences (including T2-weighted, DWI/ADC, and DCE) and PSMA PET/CT images will be preprocessed, coregistered, and fused. The model will be trained to distinguish clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions. The reference standard is histopathology from prostate biopsy or radical prostatectomy.
Outcome Measures:
Primary outcome measures include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for csPCa detection. Secondary outcome measures include positive predictive value (PPV), negative predictive value (NPV), overall accuracy, decision curve analysis (DCA) net benefit, diagnostic performance in the PSA gray zone (4-20 ng/mL) subgroup, and the proportion of patients who could avoid biopsy at 100% specificity threshold.
Statistical Analysis:
Model performance will be evaluated using internal cross-validation and external validation on data from different centers. Calibration curves and decision curve analysis will be used to assess clinical utility. Subgroup analyses will be performed for PSA gray zone patients and different Gleason grade groups.
- Conditions
- Prostate Cancer (Diagnosis)
- Keywords
- Prostate Cancer, Multiparametric MRI, PSMA PET/CT, Artificial Intelligence, Non-invasive Diagnosis
- Medical subject headings
- Prostatic Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Prostate Cancer Group | Not described | |
| Non-cancer (BPH) Control | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
The AUC (Area Under the Receiver Operating Characteristic Curve) will be calculated to evaluate the overall diagnostic performance of the AI model in distinguishing clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions. The gold standard is histopathology from prostate biopsy or radical prostatectomy.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity of the AI Model for Detecting Clinically Significant Prostate Cancer
Specificity (true negative rate) will be calculated to evaluate the model's ability to correctly identify patients without clinically significant prostate cancer. High specificity is a primary goal to reduce unnecessary prostate biopsies.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Sensitivity of the AI Model for Detecting Clinically Significant Prostate Cancer
Sensitivity (true positive rate) will be calculated to evaluate the model's ability to correctly identify patients with clinically significant prostate cancer.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Secondary outcomes (5)
Overall Diagnostic Accuracy
Overall accuracy (proportion of true results among all cases) will be calculated for the AI model in detecting clinically significant prostate cancer.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer
Net benefit will be calculated across a range of threshold probabilities to evaluate the clinical utility of the AI model for detecting clinically significant prostate cancer, using histopathological diagnosis from prostate biopsy or radical prostatectomy as the reference standard. Results will be compared with default strategies of biopsying all patients or no patients.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold
At a specificity of 100% (no false positives), the proportion of patients who would be correctly identified as non-csPCa and thus could safely avoid prostate biopsy will be calculated. Sensitivity at this threshold will also be reported.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
AUC in PSA Gray Zone (4-20 ng/mL)
The area under the receiver operating characteristic curve (AUC) of the AI model will be evaluated in patients with PSA levels between 4 and 20 ng/mL to assess discrimination between clinically significant prostate cancer and non-clinically significant or benign disease, using histopathological diagnosis as the reference standard.
Measured: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity in PSA Gray Zone (4-20 ng/mL)
Specificity of the AI model will be evaluated in patients with PSA levels between 4 and 20 ng/mL using histopathological diagnosis as the reference standard.
Measured: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2026
- Study start13 Jan 2026
- Primary completion30 Sep 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate Cancer (Diagnosis)Studies of the same condition, open and under way.
- More trials run by Xiangya Hospital of Central South UniversityThe sponsor's other registered studies.
- 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xiangya Hospital of Central South University
- Sponsor type
- University, hospital or other
- Collaborators
- Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, RenJi Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07690306
- Sponsor's protocol ID
- 2026010083_2
- Status last verified
- May 2026
- First submitted
- 16 Jun 2026
- Last updated
- 15 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Retrospective, Multicenter Study to Develop and Validate a Non-Invasive Artificial Intelligence Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT, With Pathologically Confirmed Diagnosis as the Reference Standard
- ClinicalTrials.gov ID
- NCT07690306
- Acronym / sponsor ID
- 2026010083_2
- Conditions
- Prostate Cancer (Diagnosis)
- Keywords
- Prostate Cancer, Multiparametric MRI, PSMA PET/CT, Artificial Intelligence, Non-invasive Diagnosis
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Xiangya Hospital of Central South UniversityUniversity, hospital or other
- Collaborators
- Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, RenJi Hospital
- Study start
- 13 Jan 2026 (actual)
- Primary completion
- 30 Sep 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 8 Jul 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.
Prostate cancer is one of the most common malignancies in men. Currently, due to the limited diagnostic accuracy of existing imaging tests, there is a risk of missed diagnosis or unnecessary prostate biopsy. This study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model using two advanced imaging techniques: multiparametric MRI (mpMRI) and PSMA PET/CT. By integrating information from both imaging modalities, the AI model is expected to improve the diagnostic accuracy of prostate cancer, reduce unnecessary biopsies, and assist physicians in making better clinical decisions. This is a retrospective, multicenter study that plans to collect imaging and pathology data from approximately 1,000 to 1,500 patients across six major hospitals in China. The diagnostic performance of the model will be evaluated, including its ability to identify clinically significant prostate cancer and its value in assisting diagnosis in patients with PSA levels in the gray zone (4-20 ng/mL).
Detailed description
Study Design:
This is a retrospective and prospective, multicenter, case-control study. The study aims to develop and validate a non-invasive artificial intelligence (AI) diagnostic model for prostate cancer by integrating multiparametric MRI (mpMRI) and PSMA PET/CT imaging.
Participants:
Patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at participating centers will be retrospectively enrolled. Eligible participants include pathologically confirmed prostate cancer patients (cases) and benign prostatic hyperplasia (BPH) patients (controls). Inclusion criteria include age ≥18 years, ECOG performance status 0-2, life expectancy >6 months, and availability of complete clinical data (PSA, Gleason score, PI-RADS, SUVmax, prostate volume, etc.). Exclusion criteria include prior prostate cancer treatment (endocrine therapy or radiotherapy), previous prostate surgery (e.g., TURP), severe renal insufficiency, other malignancies. Informed consent is waived for retrospective patients, while signed informed consent is required for prospective patients.
Sample Size:
Approximately 1,000 to 1,500 participants will be enrolled from six hospitals in China: Xiangya Hospital of Central South University, Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, Beijing Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine.
AI Model Development:
The AI model will be developed using deep learning and radiomics techniques. mpMRI sequences (including T2-weighted, DWI/ADC, and DCE) and PSMA PET/CT images will be preprocessed, coregistered, and fused. The model will be trained to distinguish clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions. The reference standard is histopathology from prostate biopsy or radical prostatectomy.
Outcome Measures:
Primary outcome measures include the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the AI model for csPCa detection. Secondary outcome measures include positive predictive value (PPV), negative predictive value (NPV), overall accuracy, decision curve analysis (DCA) net benefit, diagnostic performance in the PSA gray zone (4-20 ng/mL) subgroup, and the proportion of patients who could avoid biopsy at 100% specificity threshold.
Statistical Analysis:
Model performance will be evaluated using internal cross-validation and external validation on data from different centers. Calibration curves and decision curve analysis will be used to assess clinical utility. Subgroup analyses will be performed for PSA gray zone patients and different Gleason grade groups.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Prostate Cancer Group | – | – |
| Non-cancer (BPH) Control | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study will enroll patients who underwent mpMRI, PSMA PET/CT, and prostate biopsy or radical prostatectomy at six major hospitals in China: Xiangya Hospital of Central South University, Qilu Hospital of Shandong University, Chinese PLA General Hospital, The First Affiliated Hospital of Guangzhou Medical University, Beijing Hospital and Renji Hospital, Shanghai Jiao Tong University School of Medicine. The study population includes two groups: patients with pathologically confirmed prostate cancer (cases) and patients with pathologically confirmed benign prostatic hyperplasia (BPH, controls). All participants are male, aged 18 years or older, with no prior prostate cancer treatment (endocrine therapy or radiotherapy) or prostate surgery. Patients with severe renal insufficiency or other malignancies are excluded. A total of approximately 1,000 to 1,500 participants will be enrolled retrospectively from the participating centers.
Key inclusion criteria (7 listed)
- Age ≥ 18 years.
- ECOG performance status 0-2.
- Life expectancy > 6 months.
- Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export.
- Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard.
Key exclusion criteria (5 listed)
- History of other malignant tumors
- Previous prostate surgery (e.g., TURP)
- Prior endocrine therapy or radiotherapy
- Severe renal insufficiency
- Major organ dysfunction or life expectancy < 1 year
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
The AUC (Area Under the Receiver Operating Characteristic Curve) will be calculated to evaluate the overall diagnostic performance of the AI model in distinguishing clinically significant prostate cancer (csPCa) from non-csPCa or benign conditions. The gold standard is histopathology from prostate biopsy or radical prostatectomy.
Specificity of the AI Model for Detecting Clinically Significant Prostate Cancer
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity (true negative rate) will be calculated to evaluate the model's ability to correctly identify patients without clinically significant prostate cancer. High specificity is a primary goal to reduce unnecessary prostate biopsies.
Sensitivity of the AI Model for Detecting Clinically Significant Prostate Cancer
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Sensitivity (true positive rate) will be calculated to evaluate the model's ability to correctly identify patients with clinically significant prostate cancer.
5 secondary outcomes
Overall Diagnostic Accuracy
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
AUC in PSA Gray Zone (4-20 ng/mL)
Time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy
Specificity in PSA Gray Zone (4-20 ng/mL)
Time frame: At histopathological diagnosis (prostate biopsy or radical prostatectomy)
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 15 Sep 2026.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
6 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Cancer (Diagnosis), run by Xiangya Hospital of Central South University at 6 sites in 2 countries.
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 7 inclusion and 5 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: enrolling by invitation, as of the sponsor's last update on 15 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in China, United States. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer (time frame: At histopathological diagnosis by prostate biopsy or radical prostatectomy). The study lists 3 primary outcomes in all. 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 15 Sep 2026.