Study of Predicting Lymph Node Metastasis of High-risk Prostate Cancer by Artificial Intelligence Multi-omics Analysis

NCT ID: NCT07112599

Last Updated: 2026-01-09

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2026-01-25
• Study Completion Date: 2027-06-30

Brief Summary

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.

Detailed Description

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.

Conditions

Prostate Cancer; Lymph Node Cancer Metastatic; Artificial Intelligence (AI)

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

1. Age ≥ 50 years

2. Patients must have histologically or cytologically confirmed prostate adenocarcinoma

3. PSA ≥ 20ng/ml or Gleason ≥ 8

4. Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0-2

5. Life expectancy ≥ 6 months

6. Normal bone marrow function: absolute neutrophil count ≥ 1.5×109/L; platelets ≥ 75×109/L; hemoglobin ≥ 90g/L; white blood cell count ≥ 3.0×109/L

7. Normal liver function: alanine aminotransferase (ALT) or aspartate aminotransferase (AST) ≤ 2.5 times the upper limit of normal (ULN); for patients with liver metastasis, ALT/AST can be ≤ 5 times ULN

8. Total bilirubin ≤ 1.5 times ULN or total bilirubin > 1.5 times ULN and direct bilirubin ≤ ULN;

9. Normal coagulation function: International Normalized Ratio（INR） ≤ 1.5, partial thromboplastin time (APTT) ≤ 1.5 times ULN, prothrombin time (PT) < ULN + 4 seconds

10. Normal heart function: left ventricular ejection fraction (LVEF) ≥ 50%; corrected QT interval male < 450ms, female < 470ms, serum potassium ≥ 3.5mmol/L

11. Normal blood pressure: systolic blood pressure < 140mmHg, diastolic blood pressure < 90mmHg; patients with stable blood pressure assessment after appropriate clinical treatment can be enrolled

12. Normal renal function: serum creatinine ≤ 1.5 times ULN, and creatinine clearance ≥ 50 mL/min

13. Prospective subjects can understand and are willing to sign the informed consent form

14. Able to comply with the study visit schedule and other protocol requirements

Exclusion Criteria

1. Patients with contraindications to MRI examination, such as metal implants in the body, claustrophobia, etc.

2. Patients with any missing baseline clinical and pathological information

3. Patients with a clear history of neurological and psychiatric disorders, such as dementia, epilepsy, or seizures

4. In the judgment of the investigator, there are serious concomitant diseases that endanger the safety of the subjects or affect the subjects' completion of this study (such as severe diabetes, thyroid disease, and mental illness, etc.), or factors that affect the safety of the patients or affect the patients' provision of informed consent (including laboratory abnormalities), or any psychological, family, sociological or geographical conditions that affect the study plan and follow-up plan

5. The investigator believes that it is not suitable to participate in this clinical trial for any reason

6. Unable to provide informed consent

• Minimum Eligible Age: 50 Years
• Eligible Sex: MALE
• Accepts Healthy Volunteers: No

Sponsors

Anhui Medical University

OTHER

Sponsor Role: lead

Responsible Party

Sheng Tai

Chief Physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Science and Technology Institute, Anhui Medical University

Hefei, Anhui, China

Site Status: RECRUITING

Countries

China

Central Contacts

Sheng Tai

Role: CONTACT

taisheng@ahmu.edu.cn

+86 18355159268

Facility Contacts

Yihao Chen, Doctor

Role: primary

ayfykyc@126.com

+86-551-62922831

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

PJ 2025-04-44

Identifier Type: -

Identifier Source: org_study_id