AI-based Prediction of Prostate Cancer Metastasis Using Biopsy Pathology

NCT07660276 | Enrolling By Invitation DMC

• 1 other identifier: 2026010083_4
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (1)

• Prediction of Regional Lymph Node Metastasis

  Assessment of the ability of the artificial intelligence-based model using prostate cancer biopsy pathology to predict regional lymph node metastasis.

  Baseline

Study Arms (1)

Prostate Cancer Cohort

Patients with prostate cancer undergoing biopsy pathology assessment for development and validation of an AI-based metastasis prediction model.

Other: Artificial Intelligence-Based Pathology Analysis

Interventions

Artificial Intelligence-Based Pathology Analysis OTHER

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

Prostate Cancer Cohort

Eligibility Criteria

• Age: 18 Years - 90 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients with pathologically confirmed prostate cancer from multiple participating hospitals led by Xiangya Hospital. Eligible patients underwent prostate biopsy with available biopsy pathology specimens and relevant clinical and imaging data for assessment of lymph node and distant metastasis. Retrospective clinical and pathological data will be collected for development and validation of an artificial intelligence-based metastasis prediction model.

You may qualify if:

• \- Male patients aged between 18 and 90 years; Patients who underwent prostate biopsy due to elevated prostate-specific antigen (PSA), abnormal digital rectal examination (DRE), or abnormal imaging findings, were pathologically diagnosed with prostate cancer, and had available prostate biopsy pathology specimens; Patients who underwent radical prostatectomy with extended pelvic lymph node dissection (ePLND) or pelvic lymph node dissection (PLND), with definitive pathological information regarding lymph node metastasis; Patients who underwent PSMA PET/CT, MRI, bone scintigraphy, or prostate MRI capable of identifying regional lymph node metastasis or distant metastasis; Adequate cardiac, pulmonary, hepatic, and renal function; Eastern Cooperative Oncology Group (ECOG) performance status of 0-1; Expected survival time greater than 1 year; Written informed consent signed by the patient or legally authorized representative.

You may not qualify if:

• \- History of other malignancies; Severe dysfunction of major organs, including cardiac, pulmonary, hepatic, or renal insufficiency, or an expected survival time of less than 1 year; Prostate biopsy pathology specimens with inadequate whole-slide image scanning quality or failure of quality control assessment; Patients with prostate cancer diagnosed from transurethral resection specimens.

Sponsors & Collaborators

• Xiangya Hospital of Central South University: lead

Study Sites (1)

Xiangya Hospital, Central South University

Changsha, Hunan, 410008, China

Location

Study Officials

• Yi Cai

  Xiangya Hospital Central South University Department of Urology

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 16, 2026
• First Posted: June 22, 2026
• Study Start: January 8, 2026
• Primary Completion: August 1, 2026
• Study Completion: August 1, 2026
• Last Updated: June 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)