Using 3D Modeling to Detect Locally Advanced Rectal Cancer With Positive Circumferential Resection Margin
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity
研究概览
简要总结
This retrospective study aims to develop an AI-assisted 3D modeling system to improve staging accuracy for stage II-III locally advanced rectal cancer (LARC). High-quality CT images from Taichung Veterans General Hospital will be used to reconstruct tumor boundaries and spatial relationships. The AI model will be trained and validated against MRI and pathology results to predict circumferential resection margin (CRM) status. Outcomes include sensitivity, specificity, accuracy, and agreement with standard imaging. This system seeks to support precise tumor staging and inform future clinical decision-making.
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
- All
- 接受健康志愿者
- 否
入选标准
- •Diagnosed with rectal cancer, clinical stage II-III, with no distant metastasis (M0)
- •Age over 18 years, with adequate physical status classified as American Society of Anesthesiologists (ASA) I-III, capable of receiving treatment and surgery
- •No history of other malignancies or major diseases affecting study assessment within the past three years.
- •Complete medical records, including available CT and MRI imaging.
排除标准
- •Patients with clinical stage I or IV rectal cancer.
- •Age under 18 years, or physical status not meeting American Society of Anesthesiologists (ASA) I-III criteria, unable to undergo surgery or related treatment.
- •Presence of other major diseases or malignancies affecting tumor assessment (e.g., diagnosis of another malignancy within the past three years, uncontrolled cardiovascular disease).
- •Incomplete medical records or imaging data, including missing required CT or MRI images.
结局指标
主要结局
Sensitivity and specificity of the AI-assisted 3D imaging model for predicting circumferential resection margin (CRM) negativity
时间窗: Day 1 (At the time of retrospective imaging analysis)
Model predictions are compared with pathology results (gold standard) to assess diagnostic accuracy.
次要结局
- Accuracy and agreement of AI model predictions with MRI interpretations(Day 1 (At the time of retrospective imaging analysis))