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

April 25, 2024 updated by: RenJi Hospital

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Around 500 cases.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accurate diagnose the invasion depth of early esophageal squamous cell carcinoma by endoscopy NBI images through deep neural network analysis
Time Frame: 2024/12/31
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

RenJi Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 25, 2024

Primary Completion (Estimated)

May 31, 2024

Study Completion (Estimated)

December 31, 2024

Study Registration Dates

First Submitted

January 22, 2024

First Submitted That Met QC Criteria

February 1, 2024

First Posted (Actual)

February 12, 2024

Study Record Updates

Last Update Posted (Actual)

April 29, 2024

Last Update Submitted That Met QC Criteria

April 25, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LY2023-229-B

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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