NCT06463392招募中不适用
Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University1 个研究点 分布在 1 个国家目标入组 312 人开始时间: 2024年7月1日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 312
- 试验地点
- 1
- 主要终点
- Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
研究概览
简要总结
Skull-base osteonecrosis (sbORN) is a severe long-term complication of nasopharyngeal carcinoma (NPC) post radiotherapy, which significantly diminish the quality of life, increase the risk of internal carotid artery rupture, and is frequently misdiagnosed as NPC recurrence. Novel diagnostic tools are therefore clinically significant. In this study, the investigators seek to ask if a deep-learning-based model shows a significantly higher sensitivity than radiologists. With a cross-sectional design, the investigators aim to recruit 312 participants in Sun Yat-sen Memorial Hospital, Guangzhou, China that meet the eligibility criteria.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
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
- •Equal to or older than 18 years old.
- •A history of histologically confirmed nonkeratinizing undifferentiated nasopharyngeal carcinoma.
- •A history of radical radiotherapy at nasopharynx.
- •Complete remission six months post radical radiotherapy according to RECIST 1.
- •No evidence of distant metastasis upon recruitment.
- •Diagnosis of sbORN given by senior radiologist with 2-4 Likert scores.
- •Consent to biopsy awake or under general anesthesia.
- •Consent to perform blood tests, EBV DNA, EBV IgAs, and MRI inspection of nasopharynx and neck.
- •With a written consent.
排除标准
- •MRI artifacts or other factors that interfere radiological diagnosis and region of interest contouring.
- •Suspected lesion is not confined to nasopharynx and skull-base.
结局指标
主要结局
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
时间窗: Baseline
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
时间窗: Baseline
次要结局
- Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.(Baseline)
- Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.(Baseline)
- Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.(Baseline)
- Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.(Baseline)
- F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.(Baseline)
- Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.(Baseline)
- Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.(Baseline)
- Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.(Baseline)
- Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.(Baseline)
- F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.(Baseline)
- Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists.(Baseline)
- Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists.(Baseline)
研究者
研究点 (1)
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