CT-based Radiomics and Deep Learning Models to Predict Thyroid Cartilage Invasion and Patient Prognosis in Laryngeal Carcinoma

Laryngeal Carcinoma Clinical Trial

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

Status Recruiting

Clinical Trial Summary

This retrospective study was to develop and verify a CT-based radiomics model, 2D deep learning model and 3D deep learning model to preoperatively predict the thyroid cartilage invasion of laryngeal cancer patients, so as to provide more accurate diagnosis and treatment basis for clinicians. And the performance of the aforementioned models was compared with two radiologists. In addition, the researchers investigated the prediction of survival outcomes of patients by the above optimal models.

Clinical Trial Description

Laryngeal squamous cell carcinoma (LSCC), as one of the most common head and neck tumors, is the eighth leading cause of cancer-associated death worldwide. The treatment decisions has a profound impact on both tumor control and functional prognosis of LSCC patients. And these decisions are primarily based on tumor staging, with the invasion of the thyroid cartilage serving as a crucial determinant. Consequently, the presence of thyroid cartilage invasion indicates an advanced stage (T3 or T4) diagnosis for the LSCC patients. For patients without thyroid cartilage invasion, partial laryngectomy may be considered to preserve laryngeal function. However, for patients with advanced laryngeal carcinoma and thyroid cartilage invasion extending beyond the larynx, total laryngectomy is often necessary to completely remove the tumor and extend survival time. Therefore, accurate assessment of thyroid cartilage invasion is vital for treatment decision-making and prognosis evaluation for LSCC patients. Recently, artificial intelligence, in the form of machine learning and deep learning (DL), has been wildly applied in medical imaging. Radiomics, a prominent role in machine learning, can extract high-dimensional feature data and quantitatively provide valuable information for tumor staging and prognosis. In addition, DL can automatically capture and learn discriminative features through an end-to-end way for accurate prediction. Collectively, the aims of this multicenter study were to develop and validate a radiomics model, a 2D DL model, and a 3D DL model base on the venous-phase CT images, and compare the performance of these models to two radiologists in predicting thyroid cartilage invasion. Additionally, the researchers investigated the prognostic value of the optimal predicted model of thyroid cartilage invasion. ;

Related Conditions & MeSH terms

• Carcinoma
• Laryngeal Carcinoma
• Laryngeal Neoplasms
• Thyroid Diseases

• NCT number: NCT06463756
• Study type: Observational
• Source: First Affiliated Hospital of Chongqing Medical University
• Status: Recruiting
• Start date: August 13, 2023
• Completion date: October 13, 2024