Development and Validation of a Non-Invasive AI Diagnostic Model for Prostate Cancer Using Multiparametric MRI and PSMA PET/CT

NCT07690306 | Enrolling By Invitation

• 1 other identifier: 2026010083_2
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 5

Brief 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. 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).

Conditions

Prostate Cancer (Diagnosis)

Keywords

Prostate Cancer; Multiparametric MRI; PSMA PET/CT; Artificial Intelligence; Non-invasive Diagnosis

Outcome Measures

Primary Outcomes (3)

• 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.

  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.

  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.

  At histopathological diagnosis by prostate biopsy or radical prostatectomy

Secondary Outcomes (5)

• Overall Diagnostic Accuracy

  At histopathological diagnosis by prostate biopsy or radical prostatectomy

• Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer

  At histopathological diagnosis by prostate biopsy or radical prostatectomy

• Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold

  At histopathological diagnosis by prostate biopsy or radical prostatectomy

• AUC in PSA Gray Zone (4-20 ng/mL)

  At histopathological diagnosis by prostate biopsy or radical prostatectomy

• Specificity in PSA Gray Zone (4-20 ng/mL)

  At histopathological diagnosis (prostate biopsy or radical prostatectomy)

Other Outcomes (1)

• Sensitivity in PSA Gray Zone (4-20 ng/mL)

  At histopathological diagnosis (prostate biopsy or radical prostatectomy)

Study Arms (2)

Prostate Cancer Group

Non-cancer (BPH) Control

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

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.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Xiangya Hospital of Central South University: lead
• Qilu Hospital of Shandong University: collaborator
• Chinese PLA General Hospital: collaborator
• RenJi Hospital: collaborator
• The First Affiliated Hospital of Guangzhou Medical University: collaborator

Study Sites (5)

Beijing Hospital

Beijing, Beijing Municipality, 100730, China

Location

Chinese PLA General Hospital

Beijing, Beijing Municipality, 100853, China

Location

The First Affiliated Hospital of Guangzhou Medical University

Guangzhou, Guangdong, 510120, China

Location

Qilu Hospital of Shandong University

Jinan, Shandong, 250012, China

Location

Renji Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, 200127, China

Location

MeSH Terms

Conditions

Prostatic Neoplasms; Disease

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 16, 2026
• First Posted: July 8, 2026
• Study Start: January 13, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 8, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (5)