Active — Not RecruitingNot Applicable

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

China500 participantsStarted 2019-01-01

Plain-language 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.

Who can participate

Age range18 Years – 80 Years

SexALL

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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.

What they're measuring

AUROC (Area Under the Receiver Operating Characteristic Curve)

Timeframe: From enrollment to the end of treatment at 4 weeks

Trial details

NCT IDNCT07074535

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

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2026-07-30

View on ClinicalTrials.gov More Esophageal Squamous Cell Cancer (SCC) trials