Clinical trial NCT06842264: The Development and Validation of MRI-AI-based Predictive Models for CsPCa
Official title: The Development and Validation of MRI-AI-based Predictive Models for CsPCa
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Prostate Cancer
Keywords: clinically-significant prostate cancer
artificial intelligence
magnetic resonance imaging
predictive model
Lead sponsor: Peking University First Hospital
Sponsor class: OTHER
First posted: 2025-02-24
Enrollment: 3000
Sex: MALE
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

Brief 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.

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) 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 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

Primary outcomes:
measure: Biopsy pathology results
timeFrame: 1week after biopsy
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University First Hospital
Study officials:
affiliation: Dept. of Urology, Peking University First Hospital
name: Yi LIU
role: PRINCIPAL_INVESTIGATOR
References: