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

Last updated: February 18, 2025
Sponsor: Peking University First Hospital
Overall Status: Active - Recruiting

Phase

N/A

Condition

Prostate Cancer

Prostate Disorders

Urologic Cancer

Treatment

N/A

Clinical Study ID

NCT06842264
prostatemodel19-29
  • Male

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) and transitional zone-based prostate-specific antigen density (TZ-PSAD) are calculated using prostate volume and transitional zone volume. Utilizing the aforementioned data, machine learning predictive models for clinically-significant prostate cancer (csPCa) are developed and validated.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • The interval between prostate MRI and biopsy within 3 months

  • Integrity of related data

Exclusion

Exclusion Criteria:

  • PSA less than 50ng/ml

  • Any treatment for PCa prior to either MRI or biopsy, including radicalprostatectomy, radiotherapy, chemotherapy, and endocrine therapy

  • Previous history of surgical treatment or 5α-reductase inhibitor therapy for benignprostatic hyperplasia

  • Subjects undergoing MRI with an indwelling urinary catheter or suprapubic catheter

  • Inadequate quality of MRI images

Study Design

Total Participants: 3000
Study Start date:
January 01, 2024
Estimated Completion Date:
December 31, 2029

Study Description

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) and transitional zone-based prostate-specific antigen density (TZ-PSAD) are calculated using prostate volume and transitional zone volume. Utilizing the aforementioned data, machine learning predictive models for clinically-significant prostate cancer (csPCa) are developed and validated

Connect with a study center

  • Peking University First Hospital

    Beijing, 100034
    China

    Active - Recruiting

Not the study for you?

Let us help you find the best match. Sign up as a volunteer and receive email notifications when clinical trials are posted in the medical category of interest to you.