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

Peking University

Status: Enrolling

Conditions: Prostate Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT06842264

prostatemodel19-29

Details and patient eligibility

About

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.

Full 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

Enrollment: 3,000 estimated patients

Sex: Male

Volunteers: No Healthy Volunteers

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

Trial design

3,000 participants in 1 patient group

cohort 1

Description:

Cohort 1 comprises patients who underwent prostate magnetic resonance imaging (MRI) at Peking University First Hospital between January 2024 and December 2029, followed by an ultrasound-guided prostate biopsy.