AI-based Prediction of Prostate Cancer Metastasis Using Biopsy Pathology

Development and Validation of an AI-Based Metastasis Prediction Model Using Prostate Cancer Biopsy Pathology

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Prostate Cancer Metastatic
• Intervention / Treatment: Other: Artificial Intelligence-Based Pathology Analysis

• Study Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

Study Locations

• China
  • Hunan
    • Changsha, Hunan, China, 410008
      • Xiangya Hospital, Central South University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

- 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.

Exclusion Criteria:

- 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; Artificial intelligence-assisted analysis of prostate cancer biopsy pathology specimens for prediction of lymph node and distant metastasis risk.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Xiangya Hospital of Central South University
• Investigators: Principal Investigator: Yi Cai, Xiangya Hospital Central South University Department of Urology

Study record dates

Study Major Dates

• Study Start (Actual): January 8, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): August 1, 2026

Study Registration Dates

• First Submitted: June 16, 2026
• First Submitted That Met QC Criteria: June 16, 2026
• First Posted (Actual): June 22, 2026

Study Record Updates

• Last Update Posted (Actual): June 22, 2026
• Last Update Submitted That Met QC Criteria: June 16, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: 2026010083_4

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Except for essential information, no other information will be shared to protect patient privacy.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No