Multi-zone Computer-aided Prostate Segmentation on MR Images Using a Deep Learning-based Approach
试验速览
- 阶段
- 不适用
- 入组人数
- 62
- 试验地点
- 1
- 主要终点
- Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists
研究概览
简要总结
Because the diagnostic criteria for prostate cancer are different in the peripheral and the transition zone, prostate segmentation is needed for any computer-aided diagnosis system aimed at characterizing prostate lesions on magnetic resonance (MR) images. Manual segmentation is time consuming and may differ between radiologists with different expertise. We developed and trained a convolutional neural network algorithm for segmenting the whole prostate, the transition zone and the anterior fibromuscular stroma on T2-weighted images of 787 MRIs from an existing prospective radiological pathological correlation database containing prostate MRI of patients treated by prostatectomy between 2008 and 2014 (CLARA-P database).
The purpose of this study is to validate this algorithm on an independent cohort of patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Male
- 接受健康志愿者
- 否
入选标准
- •Prostate MRI contained in the PACS of the Hospices Civils de Lyon
- •Performed in 2016-2019
排除标准
- •MRIs from patients who already had treatment for prostate cancer
结局指标
主要结局
Mean Mesh Distance (Mean) between the contours of the whole prostate made by the algorithm and the two radiologists
时间窗: Month 11
The Mean Mesh Distance corresponds to the Average Boundary Distance (ABD) for each point of the reference segmentation. The distance to the closest point of the compared segmentation is first computed. Then the average of all these distances is computed and gives the ABD. The Mean Mesh Distance between the contours of the whole prostate made by the algorithm and each radiologist will be used as primary outcome measure.
次要结局
未报告次要终点