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
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 1,500
- 试验地点
- 6
- 主要终点
- Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
研究概览
简要总结
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).
详细描述
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:
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Male
- 接受健康志愿者
- 否
入选标准
- •Age ≥ 18 years.
- •ECOG performance status 0-
- •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.
排除标准
- •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
研究组 & 干预措施
Prostate Cancer Group
Non-cancer (BPH) Control
结局指标
主要结局
Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
- 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))