NCT07050576

Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma

Deep Learning and Radiomics for Prediction of Lymph Node Metastasis in Early-stage Esophageal Squamous Cell Carcinoma

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: The First Affiliated Hospital of Anhui Medical University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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.

Conditions

• ESCC
• Lymph Node Metastasis
• Radiomics

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma	The predictive performance of the model was validated in the test set. The optimal prediction model was determined based on the AUC and ACC. To assess the robustness of the chosen model, ROC analysis was conducted on the external validation set.

Timeline

• Start date: 2024-05-01
• Primary completion: 2025-10-01
• Completion: 2025-11-30
• First posted: 2025-07-03
• Last updated: 2025-07-03

Locations

1 site across 1 country: China