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NCT Number: NCT07112599

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

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.

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Key information

Age range

50 year and older

Sex eligibility

Male

Study type

Observational

Primary location

Science and Technology Institute, Anhui Medical University

Hefei, Anhui, 230022, China

Location status: Recruiting

Location contact

Yihao Chen, Doctor

CONTACT

[email protected]

+86-551-62922831

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model

    Time frame: baseline

    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

Secondary outcomes

  1. The specificity of the radiopathology artificial intelligence model

    Time frame: baseline

    The specificity of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients.

Study contacts

Contact information is provided by the study sponsor or research team.

Sponsors and collaborators

Lead sponsor

Anhui Medical University

Other

Registry information

Official study 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

Important dates

Study start
2026
Primary completion
2026
Study completion
2027
First posted
Aug 8, 2025
Registry last updated
Jan 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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