Prostate Cancer
Conditions
Keywords
clinically-significant prostate cancer, artificial intelligence, magnetic resonance imaging, predictive model
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), 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
Interventions
None listed
Sponsors
Peking University First Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Biopsy pathology results | 1week after biopsy | The pathology report will include the ISUP grade; if it is greater than or equal to 2, it is considered csPCa (clinically significant prostate cancer), otherwise, it is classified as non-csPCa. |
Countries
China
Contacts
CONTACTYi LIU
PRINCIPAL_INVESTIGATORYi LIU
Dept. of Urology, Peking University First Hospital
Outcome results
None listed