Accurate Diagnosis of the Invasion Depth in ESCC by a Deep Neural Network Analysis of NBI Endoscopy Data

Esophageal Squamous Cell Carcinoma Clinical Trial

Official title:

Accurate Diagnosis of the Invasion Depth in Early Esophageal Squamous Cell Carcinoma by a Deep Neural Network Analysis of Narrow-band Imaging Endoscopy Data

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06252974
• Other study ID #: LY2023-229-B
• Status: Not yet recruiting
• Start date: April 25, 2024
• Est. completion date: December 31, 2024

Study information

• Verified date: April 2024
• Source: RenJi Hospital
• Contact: Hui Ding, Doctor
• Phone: +8613585966417
• Email: dr_daisy[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The goal of this observational study is to accurate diagnose the stage of esophageal squamous cell carcinoma in order to help physicians to decide the appropriate clinical treatment. The main question it aims to answer is:

• To get early accurate diagnosis of the invasion depth of esophageal squamous cell carcinoma by narrow-band imaging endoscopy data.

Participants' clinical informations from routine examinations and treatments will be collected, there will be no harm to participants.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 500
• Est. completion date: December 31, 2024
• Est. primary completion date: May 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Age = 18 years old, regardless of gender;

2. Performing esophageal ESD surgery due to esophageal mucosal lesions;

3. Pathological evaluation of ESD specimens.

Exclusion Criteria:

Pathological examination after ESD surgery ruled out esophageal squamous cell carcinoma.

Related Conditions & MeSH terms

• Carcinoma
• Carcinoma, Squamous Cell
• Esophageal Squamous Cell Carcinoma

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
RenJi Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accurate diagnose the invasion depth of early esophageal squamous cell carcinoma by endoscopy NBI images through deep neural network analysis	We will compare the predictive performance of InvaDepNet before and after incorporating data generated using GAN and demonstrated that including the generated data in the training dataset effectively improves the accuracy of the predictive model. Additionally, we will train six commonly used CNN models on two datasets with different shooting angles (including NBI without magnifying and NBI with magnifying), and we will propose a ResNet model to analysis the clinical informations combine with NBI images.	2024/12/31