Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma

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.

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Key information

Conditions

ESCC Lymph Node Metastasis Lymphatic Metastasis Neoplasm Metastasis Neoplasms Neoplastic Processes Pathologic Processes Pathological Conditions, Signs and Symptoms Radiomics

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Anhui Medical University

Hefei, Anhui, 230022, China

Location status: Recruiting

Location contact

Hao Zheng, MD

CONTACT

[email protected]

+86 139 1793 6873

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients with pathologically confirmed early-stage (T1) ESCC
Preoperative contrast-enhanced CT data within 2 weeks before surgery
Without any treatment before surgical resection

Exclusion criteria

Patients who underwent neoadjuvant therapy or endoscopic treatment
Insufficient CT imaging or poor CT quality
Incomplete pathology results
Presence of metastatic disease

Treatment and study plan

The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma

Diagnostic Test

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.

Primary outcomes

AUC(the area under the curve) values of the model

Time frame: 4 years

The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC).

Study contacts

Contact information is provided by the study sponsor or research team.

Hao Zheng, MD

CONTACT

[email protected]

+86 139 1793 6873

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital of Anhui Medical University

Other

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2025
First posted: Jul 3, 2025
Registry last updated: Jul 3, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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