Prostate Cancer (Diagnosis)
Conditions
Keywords
Prostate Cancer, Multiparametric MRI, PSMA PET/CT, Artificial Intelligence, Non-invasive Diagnosis
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).
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.
Interventions
None listed
Sponsors
Xiangya Hospital of Central South University
Qilu Hospital of Shandong University
Chinese PLA General Hospital
The First Affiliated Hospital of Guangzhou Medical University
RenJi Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
MALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥ 18 years. 2. ECOG performance status 0-2. 3. Life expectancy \> 6 months. 4. Underwent mpMRI and PSMA PET/CT before systemic treatment or radical prostatectomy, with original DICOM data available for export. 5. Has pathological diagnosis from prostate biopsy or radical prostatectomy as the gold standard. 6. 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. 7. Informed consent for data use for research purposes according to each center's ethics requirements.
Exclusion criteria
1. History of other malignant tumors 2. Previous prostate surgery (e.g., TURP) 3. Prior endocrine therapy or radiotherapy 4. Severe renal insufficiency 5. Major organ dysfunction or life expectancy \< 1 year
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Curve (AUC) of the AI Model for Detecting Clinically Significant Prostate Cancer | 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 | 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 | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Overall Diagnostic Accuracy | At histopathological diagnosis by prostate biopsy or radical prostatectomy | Overall accuracy (proportion of true results among all cases) will be calculated for the AI model in detecting clinically significant prostate cancer. |
| Net Benefit of the AI Model for Detecting Clinically Significant Prostate Cancer | At histopathological diagnosis by prostate biopsy or radical prostatectomy | 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. |
| Proportion of Patients Who Could Avoid Biopsy at 100% Specificity Threshold | At histopathological diagnosis by prostate biopsy or radical prostatectomy | 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. |
| AUC in PSA Gray Zone (4-20 ng/mL) | At histopathological diagnosis by prostate biopsy or radical prostatectomy | 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. |
| Specificity in PSA Gray Zone (4-20 ng/mL) | At histopathological diagnosis (prostate biopsy or radical prostatectomy) | 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. |
Countries
China, United States
Outcome results
None listed