The Development and Validation of MRI-AI-based Predictive Models for csPCa
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 3,000
- 试验地点
- 1
- 主要终点
- Biopsy pathology results
研究概览
简要总结
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.
详细描述
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
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- Male
- 接受健康志愿者
- 否
入选标准
- •The interval between prostate MRI and biopsy within 3 months
- •Integrity of related data
排除标准
- •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
研究组 & 干预措施
cohort 1
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.
结局指标
主要结局
Biopsy pathology results
时间窗: 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.
次要结局
未报告次要终点