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

NCT07074535 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2025-07-20

No results posted yet for this study

Summary

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.

Conditions

  • Esophageal Squamous Cell Cancer (SCC)
  • Recurrent Laryngeal Nerve Palsy
  • Deep Learning
  • Postoperative Complication

Sponsors & Collaborators

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

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2026-07-30
Completion
2027-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07074535 on ClinicalTrials.gov