Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 130
- 试验地点
- 1
- 主要终点
- The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
研究概览
简要总结
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
详细描述
EUS is considered to be a more sensitive modality than CT in detecting pancreatic solid lesions due to its high spatial resolution. However, the diagnostic performance is largely dependent on the experience and the technical abilities of the practitioners. Therefore, we aim to develop an objective EUS diagnostic model based on the convolutional neural network, an artificial intelligence technique. In addition, clinical parameters such as risk factors, tumor biomarkers and radiology findings are also added to this artificial intelligence model in order to mimic the actual clinical diagnosis procedures and to increase the performance of this model.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation.
- •For each patient, all available native EUS pictures are included.
- •Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months.
排除标准
- •The image is of poor quality.
- •The images contain unique marks which can potentially bias the model, such as the biopsy needle.
结局指标
主要结局
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
时间窗: After the training process of the EUS-AI model is completed
Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model.
次要结局
- The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET(After the training process of the EUS-AI model is completed)
研究者
Bin Cheng
professor
Huazhong University of Science and Technology