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RECRUITINGOBSERVATIONAL

AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma

CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study

Important: This information is not medical advice. Talk to your doctor about whether a clinical trial is right for you.

About This Trial

This retrospective study was to develop and verify CT-based AI model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.

Who May Be Eligible (Plain English)

Who May Qualify: 1. Availability of complete clinical data 2. Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma 3. CT examination performed within 2 weeks before surgery Who Should NOT Join This Trial: 1. Patients who received preoperative chemotherapy or radiation therapy 2. CT images with significant artifacts 3. Patients with tumor recurrence Always talk to your doctor about whether this trial is right for you.

Original Eligibility Criteria

View original clinical language
Inclusion Criteria: 1. Availability of complete clinical data 2. Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma 3. CT examination performed within 2 weeks before surgery Exclusion Criteria: 1. Patients who received preoperative chemotherapy or radiation therapy 2. CT images with significant artifacts 3. Patients with tumor recurrence

Treatments Being Tested

OTHER

AI

Radiomics extracts quantitative information from medical images to generate high-dimensional feature vectors for analysis. It aims to provide insights into disease processes and improve diagnosis. Deep learning utilizes neural networks with multiple layers to learn complex patterns from data. In medical imaging, it enables accurate and efficient analysis for disease detection and diagnosis.

Locations (1)

The First Affiliated Hospital of Chongqing Medical University
Chongqing, China