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AI-based Prediction of Prostate Cancer Metastasis Using Biopsy Pathology

NCT07660276 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2026-06-22

No results posted yet for this study

Summary

This observational study aims to develop and validate an artificial intelligence-based model using prostate cancer biopsy pathology to predict lymph node metastasis and distant metastasis in patients with prostate cancer. The main questions it aims to answer are:

Can artificial intelligence-assisted analysis of prostate cancer biopsy pathology accurately predict lymph node metastasis? Can the model accurately predict distant metastasis and assess metastatic risk in patients with prostate cancer?

Researchers aim to evaluate whether the model can provide additional information for clinical decision-making and surgical planning.

Participants will:

Provide prostate biopsy pathology specimens and related clinical information; Undergo assessment of lymph node and distant metastatic status based on clinical and imaging data; Be included in the development and validation of the artificial intelligence prediction model.

Conditions

  • Prostate Cancer Metastatic

Interventions

OTHER

Artificial Intelligence-Based Pathology Analysis

Artificial intelligence-assisted analysis of prostate cancer biopsy pathology specimens for prediction of lymph node and distant metastasis risk.

Sponsors & Collaborators

  • Xiangya Hospital of Central South University

    lead OTHER

Principal Investigators

  • Yi Cai · Xiangya Hospital Central South University Department of Urology

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-01-08
Primary Completion
2026-08-01
Completion
2026-08-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07660276 on ClinicalTrials.gov