Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
NCT ID: NCT07050576
Last Updated: 2025-07-03
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
500 participants
OBSERVATIONAL
2024-05-01
2025-11-30
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
CT and Endoscopic Biopsy Image-Based Deep Learning for Predicting Left Recurrent Laryngeal Nerve Lymph Node Metastasis in Esophageal Cancer
NCT07074535
A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
NCT06829147
Mechanistic Study on the Diagnosis of Esophageal Cancer Lymph Node Metastasis Using Spectral CT, Multimodal MRI, FAPI PET-CT, Pathology, and AI Evaluation System
NCT06818214
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
NCT06831357
Deep Learning Model for Diagnosis and Contour of Cervical Lymph Node for Nasopharyngeal Carcinoma
NCT05231616
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
CASE_CONTROL
RETROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
A
A total of 400 patients with early-stage ESCC from our center were divided into training and test sets.
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.
B
A total of 100 patients with early-stage ESCC from other center were defined as external validation
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.
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
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.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Preoperative contrast-enhanced CT data within 2 weeks before surgery
* Without any treatment before surgical resection
Exclusion Criteria
* Insufficient CT imaging or poor CT quality
* Incomplete pathology results
* Presence of metastatic disease
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
The First Affiliated Hospital of Anhui Medical University
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
The First Affiliated Hospital of Anhui Medical University
Hefei, Anhui, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
Early-ESCC-LNM-2024
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.