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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Esophageal Neoplasms Malignant
• Intervention / Treatment: Diagnostic test: Magnifying Endoscopy and Endoscopic Ultrasonography

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.

• Study Type: Observational
• Enrollment (Estimated): 450

Contacts and Locations

• Study Contact: Name: Luowei Wang; Phone Number: 86-21-31161337; Email: wangluoweimd@smmu.edu.cn

Study Locations

• China
  • Shanghai, China
    • Changhai Hospital
    • Contact: Han Lin; Email: babyhan831@aliyun.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

The study was conducted in two phases. The initial stage was the modeling stage (the first stage), which included patients from six hospitals including the First Affiliated Hospital of Naval Medical University before January 1, 2024. The second phase, called real-world evaluation phase (phase 2), prospectively enrolled consecutive patients scheduled to undergo magnifying endoscopy and EUS at the aforementioned hospitals between April and June 2024.

Description

Inclusion Criteria:

Patients requiring magnifying endoscopy and endoscopic ultrasonography. Individuals of either sex, aged 18 years or older.

Exclusion Criteria:

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.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Low-grade intraepithelial neoplasia of esophageal squamous epithelium	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.
High-grade intraepithelial neoplasia of esophageal squamous epithelium	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.
Stage T1a esophageal squamous cell carcinoma	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.
Stage T1b esophageal squamous cell carcinoma	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance of models to diagnose low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, and superficial esophageal squamous carcinoma	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.	2024.04.01-2024.10.30

Collaborators and Investigators

• Sponsor: Changhai Hospital
• Collaborators: West China Hospital; Shandong Provincial Hospital; The First Affiliated Hospital of Soochow University; The First Affiliated Hospital of Henan University of Science and Technology; THE FIRST AFFILIATED HOSPITAL OF SHIHEZI UNIVERSITY
• Investigators: Study Chair: Luowei Wang, Changhai Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): May 15, 2024
• Primary Completion (Estimated): August 30, 2024
• Study Completion (Estimated): October 30, 2024

Study Registration Dates

• First Submitted: May 6, 2024
• First Submitted That Met QC Criteria: May 10, 2024
• First Posted (Actual): May 14, 2024

Study Record Updates

• Last Update Posted (Actual): May 14, 2024
• Last Update Submitted That Met QC Criteria: May 10, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Keywords: Esophageal Neoplasms; Endosonography; multimodality
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms by Histologic Type; Neoplasms by Site; Carcinoma; Neoplasms, Glandular and Epithelial; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Head and Neck Neoplasms; Esophageal Diseases; Neoplasms, Squamous Cell; Neoplasms; Carcinoma, Squamous Cell; Esophageal Neoplasms; Esophageal Squamous Cell Carcinoma
• Other Study ID Numbers: MEIFI-sESCC

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No