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

Official title

Clinical Study on Predicting Lymph Node Metastasis of High-risk Prostate Cancer Based on Artificial Intelligence Multi-omics Analysis：A Multicenter, Prospective and Observational Clinical Study

Overview

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.

Primary outcome measures

• The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model [Time frame: baseline]

Secondary outcome measures (1)

• The specificity of the radiopathology artificial intelligence model [Time frame: baseline]

Eligibility criteria

Inclusion criteria

• Age ≥ 50 years
• Patients must have histologically or cytologically confirmed prostate adenocarcinoma
• PSA ≥ 20ng/ml or Gleason ≥ 8
• Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0-2
• Life expectancy ≥ 6 months
• 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
• 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
• Total bilirubin ≤ 1.5 times ULN or total bilirubin > 1.5 times ULN and direct bilirubin ≤ ULN;
• Normal coagulation function: International Normalized Ratio（INR） ≤ 1.5, partial thromboplastin time (APTT) ≤ 1.5 times ULN, prothrombin time (PT) < ULN + 4 seconds
• Normal heart function: left ventricular ejection fraction (LVEF) ≥ 50%; corrected QT interval male < 450ms, female < 470ms, serum potassium ≥ 3.5mmol/L
• Normal blood pressure: systolic blood pressure < 140mmHg, diastolic blood pressure < 90mmHg; patients with stable blood pressure assessment after appropriate clinical treatment can be enrolled
• Normal renal function: serum creatinine ≤ 1.5 times ULN, and creatinine clearance ≥ 50 mL/min
• Prospective subjects can understand and are willing to sign the informed consent form
• Able to comply with the study visit schedule and other protocol requirements

Exclusion criteria

• Patients with contraindications to MRI examination, such as metal implants in the body, claustrophobia, etc.
• Patients with any missing baseline clinical and pathological information
• Patients with a clear history of neurological and psychiatric disorders, such as dementia, epilepsy, or seizures
• 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
• The investigator believes that it is not suitable to participate in this clinical trial for any reason
• Unable to provide informed consent

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Other

Study locations

China · 1 center

• Science and Technology Institute, Anhui Medical University — Hefei

Identifiers

NCT: NCT07112599 · PJ 2025-04-44

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗