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The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. The model determines whether the patient has lymph node metastasis based on the MRI results and the pathological section image information of the case combined with clinical data before radical resection of the prostate. This study is a multicenter, prospective clinical study to verify the model's ability to predict whether high-risk prostate cancer patients may have lymph node metastasis.
This is a multicenter, prospective clinical study designed to validate the radiopathology artificial intelligence (AI) model. The study will recruit patients with prostate cancer from the First Affiliated Hospital of Anhui Medical University, Nanjing Drum Tower Hospital, Cancer Hospital of Chinese Academy of Medical Sciences, Hospital General University Gregorii Maran and the First Affiliated Hospital of Bengbu Medical University, with Gleason score ≥8 or prostate specific antigen (PSA)≥20ng/ml. In addition, MRI examinations are required before prostate biopsy, and pathological sections are scanned after radical prostatectomy. Experienced radiologists and pathologists manually outline the tumor region of interest (ROI) on the image. The outlined MRI information and pathological section scan information are input into the model to obtain the probability of lymph node metastasis in the patient. Whether lymph node metastasis occurs is determined by pelvic lymph node dissection specimens. By comparing the probability of lymph node metastasis predicted by the model with the actual situation, the researchers calculate the predicted sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy. This study verifies the high accuracy of the radiopathology AI model in predicting lymph node metastasis in patients with high-risk prostate cancer, and provides a basis for the precise treatment of high-risk prostate cancer patients.
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| Measure | Description | Time Frame |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model | The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients | baseline |
| Measure | Description | Time Frame |
|---|---|---|
| The specificity of the radiopathology artificial intelligence model | The specificity of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients. | baseline |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with high-risk prostate cancer (PSA ≥ 20ng/ml or Gleason ≥ 8)
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sheng Tai | Contact | +86 18355159268 | taisheng@ahmu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Science and Technology Institute, Anhui Medical University | Recruiting | Hefei | Anhui | 230022 | China |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 11, 2025 | Aug 13, 2025 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Aug 11, 2025 | Aug 13, 2025 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D011471 | Prostatic Neoplasms |
| ID | Term |
|---|---|
| D005834 | Genital Neoplasms, Male |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| D005832 |
| Genital Diseases, Male |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D011469 | Prostatic Diseases |
| D052801 | Male Urogenital Diseases |