Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 100,000
- 试验地点
- 1
- 主要终点
- Accuracy
研究概览
简要总结
This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;
- •Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);
- •Availability of complete pre-treatment non-contrast CT imaging data.
排除标准
- •Non-diagnostic image quality;
- •Absence of a definitive reference-standard diagnosis;
- •Incomplete clinical or imaging data.
研究组 & 干预措施
Positive Group / Malignant Cohort
Negative Control Group I / Benign Cohort
Negative Control Group II / Healthy Cohort
结局指标
主要结局
Accuracy
时间窗: 1.5 years
Proportion of correct classifications
Sensitivity
时间窗: 1.5years
Proportion of true positive cases
Specificity
时间窗: 1.5years
Proportion of true negative cases
次要结局
- Delta Sensitivity (AI-assisted vs. unassisted)(1.5years)
- Delta Specificity (AI-assisted vs. unassisted)(1.5years)
- Delta Accuracy (AI-assisted vs. unassisted)(1.5years)
研究者
Lian Yang
Director
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology