NCT06412419

Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma

Based on Multimodal Endoscopy and Weakly Supervised Deep Learning-Early Esophageal Squamous Cell Carcinoma Infiltration Depth Precise Prediction Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 450 (estimated)
• Sponsor: Changhai Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

Summary

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.

Detailed description

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.

Conditions

• Esophageal Neoplasms Malignant

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Magnifying Endoscopy and Endoscopic Ultrasonography	The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.

Timeline

• Start date: 2024-05-15
• Primary completion: 2024-08-30
• Completion: 2024-10-30
• First posted: 2024-05-14
• Last updated: 2024-05-14

Locations

1 site across 1 country: China