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

Army Medical University of People's Liberation Army

Status: Active, not recruiting

Conditions: Deep Learning; Postoperative Complication; Esophageal Squamous Cell Cancer (SCC); Recurrent Laryngeal Nerve Palsy

Study type: Observational

Funder types: Other

Identifiers

NCT07074535

CEDREL-LNM

Details and patient eligibility

About

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.

Enrollment: 500 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

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.

Trial design

500 participants in 2 patient groups

Left RLN lymph node （+）

Description:

Postoperative pathological results confirmed the metastasis of left recurrent laryngeal nerve lymph nodes.

Left RLN lymph node （-）

Description:

Postoperative pathological results confirmed the negative of left recurrent laryngeal nerve lymph nodes.