CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Laryngeal Carcinoma
- Sponsor
- First Affiliated Hospital of Chongqing Medical University
- Enrollment
- 400
- Locations
- 1
- Primary Endpoint
- Area under the curve, AUC
- Status
- Recruiting
- Last Updated
- last year
Overview
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.
Investigators
xinwei Chen
Radiology Department
First Affiliated Hospital of Chongqing Medical University
Eligibility Criteria
Inclusion Criteria
- •Availability of complete clinical data
- •Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
- •CT examination performed within 2 weeks before surgery
Exclusion Criteria
- •Patients who received preoperative chemotherapy or radiation therapy
- •CT images with significant artifacts
- •Patients with tumor recurrence
Outcomes
Primary Outcomes
Area under the curve, AUC
Time Frame: 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 Outcomes
- 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)