CT and Endoscopic Biopsy Image-Based Deep Learning for Predicting Left Recurrent Laryngeal Nerve Lymph Node Metastasis in Esophageal Cancer

The goal of this observational study is to develop a predictive model for left recurrent laryngeal nerve (RLN) lymph node metastasis using deep learning algorithms. The model will be developed using clinical data from previous esophageal cancer surgeries, including preoperative CT imaging, and histopathological images from gastroscopic biopsies. The model will also be validated through prospective clinical trials to guide the intraoperative lymph node dissection, thereby reducing postoperative risks of RLN injury.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Esophageal Squamous Cell Cancer (SCC) Carcinoma Carcinoma, Squamous Cell Cranial Nerve Diseases Deep Learning Digestive System Diseases Digestive System Neoplasms Esophageal Diseases Esophageal Neoplasms Esophageal Squamous Cell Carcinoma Gastrointestinal Diseases Gastrointestinal Neoplasms Head and Neck Neoplasms Laryngeal Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Neoplasms, Squamous Cell Nervous System Diseases Neurologic Manifestations Otorhinolaryngologic Diseases Paralysis Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complication Postoperative Complications Recurrent Laryngeal Nerve Palsy Respiratory Tract Diseases Signs and Symptoms Vagus Nerve Diseases Vocal Cord Paralysis

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Daping Hospital

Chongqing, China

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Preoperative gastroscopic biopsy confirmed esophageal squamous cell carcinoma；
The patient underwent esophagectomy with lymph nodes dissection along the left recurrent laryngeal nerve.

Exclusion criteria

The patient's medical records are incomplete；
The patient refused to participate in the trial.

Treatment and study plan

Primary outcomes

AUROC (Area Under the Receiver Operating Characteristic Curve)

Time frame: From enrollment to the end of treatment at 4 weeks

The discriminant ability of the comprehensive evaluation model at different thresholds (positive vs. negative)

Sponsors and collaborators

Lead sponsor

Daping Hospital and the Research Institute of Surgery of the Third Military Medical University

Other

Registry information

Important dates

Study start: 2019
Primary completion: 2026
Study completion: 2027
First posted: Jul 20, 2025
Registry last updated: Jul 20, 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.

Completed