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

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.

Study Overview

• Status: Recruiting
• Conditions: Laryngeal Carcinoma; Thyroid Cartilage
• Intervention / Treatment: Other: AI

Detailed 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.

• Study Type: Observational
• Enrollment (Estimated): 400

Contacts and Locations

Study Locations

• China
  • Chongqing, China
    • Recruiting
    • The First Affiliated Hospital of Chongqing Medical University
    • Contact: Peng juan; Phone Number: +86 189 8328 0171; Email: pengjuan1209@126.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

The investigators collected patients with laryngeal carcinoma from two centers.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
training cohort; No interventions	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.; Other Names: radiomics; deep learning
internal validation cohort; No interventions	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.; Other Names: radiomics; deep learning
external validation cohort; No interventions	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.; Other Names: radiomics; deep learning

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area under the curve, AUC	Area under the curve(AUC) is a metric widely used in machine learning and medical research to evaluate the performance of models in binary classification problems. It reflects the ability of a model to identify true positives (True Positives) while avoiding falsely classifying negative examples as positive (False Positives).	Through study completion, an average of 6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Disease-Free-Survival, DFS	Disease-Free Survival (DFS) refers to the time from the start of randomization (usually the starting point of a clinical trial) to the recurrence of the disease or death of the patient due to disease progression. DFS is an important clinical and statistical indicator used to evaluate the long-term effects of cancer treatment.	The date of surgery and the occurrence of events such as disease progression, the date of the last follow-up, or death from any cause, and the follow-up time was at least 3 years

Collaborators and Investigators

• Sponsor: First Affiliated Hospital of Chongqing Medical University
• Collaborators: Nankai University

Study record dates

Study Major Dates

• Study Start (Actual): August 13, 2023
• Primary Completion (Estimated): September 13, 2024
• Study Completion (Estimated): October 13, 2024

Study Registration Dates

• First Submitted: June 12, 2024
• First Submitted That Met QC Criteria: June 17, 2024
• First Posted (Actual): June 18, 2024

Study Record Updates

• Last Update Posted (Actual): August 22, 2024
• Last Update Submitted That Met QC Criteria: August 20, 2024
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Keywords: deep learning; radiomics
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms by Histologic Type; Neoplasms; Neoplasms by Site; Neoplasms, Glandular and Epithelial; Endocrine System Diseases; Respiratory Tract Neoplasms; Otorhinolaryngologic Neoplasms; Head and Neck Neoplasms; Otorhinolaryngologic Diseases; Laryngeal Diseases; Carcinoma; Thyroid Diseases; Laryngeal Neoplasms
• Other Study ID Numbers: 2024-Chenx

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: The clinical data are manually collected from the clinical case system; the CT image data are exported from the PACS system and anonymously stored on a separate data disk; and the image materials are collected and anonymously stored on a separate data disk.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No