AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma

Recruiting

• Conditions: Thyroid Cartilage; Laryngeal Carcinoma
• Interventions: Other: AI

• Registration Number: NCT06463756
• Lead Sponsor: First Affiliated Hospital of Chongqing Medical University

Brief 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.

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 Timeline

• Study Start: 2023-08-13
• Study First Submitted: 2024-06-12
• Study First Submitted QC: 2024-06-17
• Study First Posted: 2024-06-18
• Status Verified: 2024-08
• Last Update Submitted: 2024-08-20
• Last Update Posted: 2024-08-22
• Primary Completion: 2024-09-13
• Study Completion: 2024-10-13

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
training cohort	AI	No interventions
external validation cohort	AI	No interventions
internal validation cohort	AI	No interventions

Primary Outcome Measures

Name	Time	Method
Area under the curve, AUC	Through study completion, an average of 6 months	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).

Secondary Outcome Measures

Name	Time	Method
Disease-Free-Survival, DFS	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	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.

Trial Locations

Locations (1)

The First Affiliated Hospital of Chongqing Medical University; 🇨🇳 Chongqing, China