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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06252974

Enrollment

500

Registered

2024-02-12

Start date

2024-04-25

Completion date

2024-12-31

Last updated

2024-04-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Esophageal Squamous Cell Carcinoma

Brief 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.

Interventions

None listed

Study design

Observational model

CASE_ONLY

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Accurate diagnose the invasion depth of early esophageal squamous cell carcinoma by endoscopy NBI images through deep neural network analysis	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.

Contacts

Primary ContactHui Ding, Doctor

dr_daisy@163.com+8613585966417

Outcome results