Deep Learning and Radiomics for Prediction of Lymph Node Metastasis in Early-stage Esophageal Squamous Cell Carcinoma
Status: Recruiting
Location: See location...
Intervention Type: Diagnostic test
Study Type: Observational
SUMMARY
This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.
Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: f
View:
• Patients with pathologically confirmed early-stage (T1) ESCC
• Preoperative contrast-enhanced CT data within 2 weeks before surgery
• Without any treatment before surgical resection
Locations
Other Locations
China
The First Affiliated Hospital of Anhui Medical University
RECRUITING
Hefei
Contact Information
Primary
Hao Zheng, MD
pojunayfy@gmail.com
+86 139 1793 6873
Time Frame
Start Date: 2024-05-01
Estimated Completion Date: 2025-11-30
Participants
Target number of participants: 500
Treatments
A
A total of 400 patients with early-stage ESCC from our center were divided into training and test sets.
B
A total of 100 patients with early-stage ESCC from other center were defined as external validation
Related Therapeutic Areas
Sponsors
Leads: The First Affiliated Hospital of Anhui Medical University