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The Development and Validation of MRI-AI-based Predictive Models for csPCa

NCT06842264 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2026-01-29

No results posted yet for this study

Summary

This study retrospectively included patients who underwent prostate magnetic resonance imaging (MRI) and subsequent ultrasound-guided prostate biopsy at Peking University First Hospital from January 2019 to December 2023, and prospectively enrolls patients from January 2024 to December 2029. Clinical information such as age, PSA levels, PI-RADS scores, and digital rectal examination findings are collected. A well-performing artificial intelligence model is employed to measure prostate volume, transitional zone volume, and lesion volume using MRI images. Furthermore, prostate-specific antigen density (PSAD), transitional zone-based prostate-specific antigen density (TZ-PSAD) and lesion-based prostate-specific antigen density (lesion-PSAD) are calculated using prostate volume, transitional zone volume and lesion volume. Utilizing the aforementioned data, machine learning predictive models for clinically-significant prostate cancer (csPCa) are developed and validated.

Conditions

Sponsors & Collaborators

  • Peking University First Hospital

    lead OTHER

Principal Investigators

  • Yi LIU · Dept. of Urology, Peking University First Hospital

Eligibility

Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2029-12-31
Completion
2029-12-31

Countries

  • China

Study Locations

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Entities

Diseases

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 NCT06842264 on ClinicalTrials.gov