AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma

NCT06463756 | Recruiting

• 1 other identifier: 2024-Chenx
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

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.

Conditions

Laryngeal Carcinoma; Thyroid Cartilage

Keywords

radiomics; deep learning

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• 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

Study Arms (3)

training cohort

No interventions

Other: AI

internal validation cohort

No interventions

Other: AI

external validation cohort

No interventions

Other: AI

Interventions

AI OTHER

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.

Also known as: radiomics, deep learning

external validation cohort; internal validation cohort; training cohort

Eligibility Criteria

• Age: 18 Years - 81 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The investigators collected patients with laryngeal carcinoma from two centers.

You may qualify if:

• Availability of complete clinical data
• Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
• CT examination performed within 2 weeks before surgery

You may not qualify if:

• Patients who received preoperative chemotherapy or radiation therapy
• CT images with significant artifacts
• Patients with tumor recurrence

Sponsors & Collaborators

• First Affiliated Hospital of Chongqing Medical University: lead
• Nankai University: collaborator

Study Sites (1)

The First Affiliated Hospital of Chongqing Medical University

Chongqing, China

RECRUITING

MeSH Terms

Conditions

Laryngeal Neoplasms

Interventions

Deep Learning

Condition Hierarchy (Ancestors)

Otorhinolaryngologic Neoplasms; Head and Neck Neoplasms; Neoplasms by Site; Neoplasms; Laryngeal Diseases; Respiratory Tract Diseases; Respiratory Tract Neoplasms; Otorhinolaryngologic Diseases

Intervention Hierarchy (Ancestors)

Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Radiology Department

Study Record Dates

• First Submitted: June 12, 2024
• First Posted: June 18, 2024
• Study Start: August 13, 2023
• Primary Completion: September 13, 2024
• Study Completion: October 13, 2024
• Last Updated: August 22, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)