Investigation of Impact of AI on Prostate Cancer Workflow
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 150
- 试验地点
- 1
- 主要终点
- Readers' (radiologists') mean quadrant-level area under the receiver operating characteristic curve (AUC) in predicting the presence or absence of clinically significant prostate cancer (csPCa)
研究概览
简要总结
This study will enroll participants who are undergoing an MRI before a prostate biopsy due to suspected prostate cancer. The purpose of this study is to see if the use of Artificial Intelligence (AI) helps detect lesions on an MRI better than a radiologist not using AI. The AI Rad Companion (AIRC) Prostate MRI application is a software that uses measurements of the prostate and will be utilized in this study to help detect potential cancerous lesions. The AI software will assign the lesions a PI-RADS score, which is a way to measure the chance of the lesion being cancer.
There are two parts to this study. The first part involves comparing the interpretation of prostate MRI images by a radiologist alone, a radiologist aided by AI, and AI alone. A systematic biopsy will be completed per standard of care. The radiologist may opt to include up to 2 additional AI-identified targets to biopsy in addition to those biopsied for standard of care.
The second part of the study involves utilizing the MRI images from the first part of the study in addition to retrospective prostate MRI images. These de-identified images, along with Prostate Image Quality (PI-QUAL) scores, clinical data, and biopsy results will be sent to Siemens in order to aid in the development of methods to identify good or bad image quality in prostate MRI images.
详细描述
Prostate cancer is the most diagnosed cancer among men in the United States and the second most prevalent cancer in men worldwide (1,2). MRI can better triage patients to undergo biopsy, while providing information about tumors and treatment progress (4). However, there are challenges with MRI image quality, the interpretation of the images, and the potential for false positives and negatives. With the annual number of prostate exams rapidly increasing (5), there is a need for accurate and reliable prostate MRI interpretation. One proposed approach is to use an AI-based lesion detection software in addition to radiologists' interpretations. The Siemens Healthineers ("Siemens") business lines Digital & Automation ("D&A") and Magnetic Resonance ("MR") have developed the AI Rad Companion Prostate MRI (AIRC), which aids in the detection of potential prostate cancer. The goal of this study is to investigate the effects of the use of AIRC in improving the standard of care in prostate cancer detection and treatment. Participation in the study will be about an hour to review the consent form. All other study procedures (MRI, biopsy) will be standard of care.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 55 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Plan of care is to undergo a biopsy of the prostate after a pre-biopsy MRI
- •Age 55-80
- •Prostate-specific antigen (PSA) between 3-10 ng/mL
- •No prior diagnosis or treatment of prostate cancer
排除标准
- •Pre-biopsy MRI is of low quality
- •PI-QUAL score of 1 using PI-QUAL version 2
研究组 & 干预措施
AI-aided MRI & Prostate Biopsy
干预措施: AI (Device)
结局指标
主要结局
Readers' (radiologists') mean quadrant-level area under the receiver operating characteristic curve (AUC) in predicting the presence or absence of clinically significant prostate cancer (csPCa)
时间窗: One-time MRI, up to 30 days post-enrollment in study.
csPCa is defined as Gleason grade group ≥ 2.
次要结局
- Inter-reader agreement on the presence/absence of csPCa at the participant-level(One-time MRI, up to 30 days post-enrollment in study.)
- Inter-reader agreement on the presence/absence of csPCa at the quadrant-level(One-time MRI, up to 30 days post-enrollment in study.)
- Participant-level sensitivity for detection of clinically significant prostate cancer(One-time MRI, up to 30 days post-enrollment in study.)
- Participant-level specificity for detection of clinically significant prostate cancer(One-time MRI, up to 30 days post-enrollment in study.)
- Quadrant-level sensitivity for detection of clinically significant prostate cancer(One-time MRI, up to 30 days post-enrollment in study.)
- Quadrant-level specificity for detection of clinically significant prostate cancer(One-time MRI, up to 30 days post-enrollment in study.)
- Free-response ROC curve for the aided and unaided reads(One-time MRI, up to 30 days post-enrollment in study.)
- Comparison of the quadrant-level AUC of the unaided vs AI read(One-time MRI, up to 30 days post-enrollment in study.)