Based on Multimodal Endoscopy and Weakly Supervised Deep Learning-Early Esophageal Squamous Cell Carcinoma Infiltration Depth Precise Prediction Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 450
- 试验地点
- 1
- 主要终点
- Performance of models to diagnose low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and superficial esophageal squamous carcinoma
研究概览
简要总结
The objective of this project is to pioneer a novel protocol for the adjunctive screening of early-stage esophageal cancer and its precancerous lesions. The anticipated outcomes include simplifying the training process for users, shortening the duration of examinations, and achieving a more precise assessment of the extent of esophageal cancer invasion than what is currently possible with ultrasound technology. This research endeavors to harness the synergy of endoscopic ultrasound (EUS) and Magnifying endoscopy, augmented by the pattern recognition and correlation capabilities of artificial intelligence (AI), to detect early esophageal squamous cell carcinoma and its invasiveness, along with high-grade intraepithelial neoplasia. The overarching goal is to ascertain the potential and significance of this approach in the early detection of esophageal cancer.
The project's primary goals are to develop three distinct AI-assisted diagnostic systems:
An AI-driven electronic endoscopic diagnosis system designed to autonomously identify lesions.
An AI-based EUS diagnostic system capable of automatically delineating the affected areas.
A multimodal diagnostic framework that integrates electronic endoscopy with EUS to enhance diagnostic accuracy and efficiency.
详细描述
The study was executed in two distinct phases. The initial phase, designated as the modeling phase (Phase 1), involved a retrospective analysis of eligible subjects from a consortium of medical institutions, including the First Affiliated Hospital of Naval Medical University, West China Hospital of Sichuan University, Provincial Hospital Affiliated to Shandong First Medical University, the First Affiliated Hospital of Soochow University, the First Affiliated Hospital of Henan University of Science and Technology, and the First Affiliated Hospital of Shihezi University, all selected prior to January 1, 2024. The second phase, known as the real-world evaluation phase (Phase 2), prospectively enrolled consecutive patients who were scheduled to undergo magnometric endoscopy and EUS at the aforementioned hospitals between April 2024 and June 2024.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
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
- •Patients requiring magnifying endoscopy and endoscopic ultrasonography. Individuals of either sex, aged 18 years or older.
排除标准
- •Inability to complete esophageal electronic endoscopy. Absence of biopsy or surgery, resulting in unobtainable pathological results. Patients who have undergone endoscopic lesion destruction or piecemeal resection, preventing the acquisition of an en bloc resection sample.
- •Patients with significant endoscopic, imaging, or pathological evidence of advanced esophageal cancer.
- •Patients presenting with marked esophageal stenosis or dilatation. Individuals with a history of other malignancies. Patients who have received neoadjuvant radiotherapy. Patients who declined to participate in the study and did not provide informed consent.
结局指标
主要结局
Performance of models to diagnose low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and superficial esophageal squamous carcinoma
时间窗: 2024.04.01-2024.10.30
Endoscopic Submucosal Dissection (ESD) serving as the gold standard. Computation of sensitivity and specificity involves the use of four fundamental metrics: true positive (TP), true negative (TN), false negative (FN), and false positive (FP). Subsequently, the Area Under the Curve (AUC) is utilized to assess the diagnostic efficacy of the model.
次要结局
未报告次要终点