Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 86
- 试验地点
- 1
- 主要终点
- Negative predictive values(NPV)
研究概览
简要总结
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.
详细描述
Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.
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 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patients aged 18-75 years who undergo the colonoscopy examination and biopsy
排除标准
- •patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy
- •patients with previous surgical procedures on the gastrointestinal tract.
- •patients with contraindications to biopsy
- •patients who refuse to sign the informed consent form
结局指标
主要结局
Negative predictive values(NPV)
时间窗: 1 year
Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)\*100%
Specificity
时间窗: 1 year
Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer
时间窗: 1 year
Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%
Sensitivity
时间窗: 1 year
Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.
AUC (95% CI)
时间窗: 1 year
area under the receiver operating characteristic curve (AUC)
次要结局
- To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition(1 year)
研究者
Xiuli Zuo
Director of Qilu Hospital gastroenterology department
Shandong University