D-Lung: An Analytics Platform for Primary Lung Cancer Screening, Diagnosis and Management Based on Deep Learning Technology
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
- accuracy
研究概览
简要总结
Lung cancer is one of main cause of cancer death in worldwide, characterized of low 5-year survival rate of less than 20%. Pulmonary nodule is considered as the typical imaging manifestation in early stage of lung cancer. The National Lung Screen Trial has demonstrated that the mortality rates could decline greatly, by the utility of low-dose helical computed tomography for screen of pulmonary nodules. Thus, automatic detection, diagnosis and management of pulmonary nodules, play the vital roles in computer-aided lung cancer screening and early intervention.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Subjects with suspicious lung nodules.
- •Thin-layer thoracic CT and pathology examination have been performed for suspicious lung nodules.
排除标准
- •Subjects with accompanied lesions on CT images that may interfere to lung nodules analysis
结局指标
主要结局
accuracy
时间窗: 2 years
proportion of true results(both true positives and true negatives) among whole instances
sensitivity
时间窗: 2 years
true positive rate in percentage(%) derived by ROC analysis
specificity
时间窗: 2 years
true negative rate in percentage (%) derived by ROC analysis
area under curve (AUC)
时间窗: 2 years
area under ROC curve in percentage (%)
次要结局
- average number of false positives per scan (FPs/scan)(2 years)
- competition performance metric (CPM)(2 years)
研究者
Professor Winnie W.C. Chu
Professor
Chinese University of Hong Kong