Deep Learning, Esophageal Squamous Cell Cancer (SCC), Postoperative Complication, Recurrent Laryngeal Nerve Palsy
Conditions
Brief 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.
Interventions
None listed
Sponsors
Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUROC (Area Under the Receiver Operating Characteristic Curve) | From enrollment to the end of treatment at 4 weeks | The discriminant ability of the comprehensive evaluation model at different thresholds (positive vs. negative) |
Countries
China
Outcome results
None listed