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Recruiting NCT06842264

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

— Observational Prostate Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Prostate Cancer. Basic parameters: No limits · Male.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Overview

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.

Detailed 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), 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

Primary outcome measures

  • Biopsy pathology results [Time frame: 1week after biopsy]

Eligibility criteria

Inclusion criteria

  • The interval between prostate MRI and biopsy within 3 months
  • Integrity of related data

Exclusion criteria

  • PSA less than 50ng/ml
  • Any treatment for PCa prior to either MRI or biopsy, including radical prostatectomy, radiotherapy, chemotherapy, and endocrine therapy
  • Previous history of surgical treatment or 5α-reductase inhibitor therapy for benign prostatic hyperplasia
  • Subjects undergoing MRI with an indwelling urinary catheter or suprapubic catheter
  • Inadequate quality of MRI images

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Peking University First Hospital — Beijing

Identifiers

NCT: NCT06842264 · prostatemodel19-29

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗