Laryngeal Carcinoma, Thyroid Cartilage
Conditions
Keywords
radiomics, deep learning
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.
Interventions
OTHERAI
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.
Sponsors
Nankai University
First Affiliated Hospital of Chongqing Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 81 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed