Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors.
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 600
- 试验地点
- 5
- 主要终点
- Sensitivity and specificity of CADx algorithm
研究概览
简要总结
In women with an ovarian tumor, it is often unclear whether the tumor is benign or malignant. To differentiate, tumor markers (CA125 and CEA), a transvaginal ultrasound and, depending on the ultrasound image and the CA125 concentration, a CT scan are performed. The quality of radiological imaging in diagnosing abdominal pathology is often not accurate enough, making additional interventions no-dig for proper classification and interpretation of the tumor.
Objective: To improve accuracy for distinguishing benign from malignant disease in patients presenting with an ovarian mass by using a computer aided detection algorithm.
详细描述
This research focuses on improving the accuracy of the determination of the nature (benign or malignant) of ovarian tumors by making use of artificial intelligence by creating a CT-scan algorithm. This because a correct preoperative classification of ovarian tumors is essential for appropriate treatment. Existing prediction models often lead to unnecessary referrals to gynecological oncology hospitals, resulting in higher costs and increased stress for the patient. It is therefore important to evaluate other strategies to differentiate between benign and malignant ovarian tumors.
Artificial Intelligence (AI) for radiology is currently being developed by the Eindhoven University of Technology (TU/e) and Philips Research Europe and may provide a potential solution to this problem.
The currently developed algorithm (CADx), using a support vector machine (SVM), showed within a small population of about 100 patients a sensitivity of 74% and specificity of 74%. These are promising results to train this algorithm even further with more CT-scans images and the addition of clinical variables and even liquid biopsies.
Type of study: Retrospective study cohort This is a retrospective analysis on known data in which definitive patients diagnosis has already been established and current analysis will not affect treatment plan.
No products for patients are used, only computer aided diagnosis is used on existing radiological imaging, namely CT-scans.
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
- Female
- 接受健康志愿者
- 否
入选标准
- •patients with an ovarian tumor of which it is unknown whether it is benign or malignant (Risk of Malignancy Index (RMI) >200)
- •underwent surgery
- •histological proof of tumor
排除标准
- •indefinite pathology report
- •lack of correct description of staging in OR report when applicable
结局指标
主要结局
Sensitivity and specificity of CADx algorithm
时间窗: 3 - 4 years
Percentage of correct determination of malignancy by the Risk of Malignancy Index (RMI) compared to exact determination by CAD assessment in patients with an ovarian tumor
次要结局
- Sensitivity and specificity of CADx algorithm with additional variables(3 - 4 years)
研究者
Jurgen M.J. Piek
MD-PhD
Gynaecologisch Oncologisch Centrum Zuid