RecruitingNCT06463756

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

Sponsor

First Affiliated Hospital of Chongqing Medical University

Enrollment

400 participants

Start Date

Aug 13, 2023

Study Type

OBSERVATIONAL

Conditions

Laryngeal Carcinoma Thyroid Cartilage

Summary

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.

Eligibility

Min Age: 18 YearsMax Age: 81 Years

Plain Language Summary

Simplified for easier understanding

This study develops and tests an AI model that uses CT scan images to predict whether laryngeal (voice box) cancer has invaded the thyroid cartilage — important information that guides decisions between surgery and radiation therapy for laryngeal cancer. **You may be eligible if...** - You have been diagnosed with laryngeal squamous cell carcinoma (voice box cancer) confirmed by surgery or biopsy - You had a CT scan performed within 2 weeks before surgery - You have complete clinical data available **You may NOT be eligible if...** - You received chemotherapy or radiation therapy before surgery - Your CT images have significant artifacts that reduce image quality - Your cancer is a recurrence rather than a first-time diagnosis Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

OTHERAI

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

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NCT06463756

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