AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-81
SponsorFirst Affiliated Hospital of Chongqing Medical University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Availability of complete clinical data
Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma
CT examination performed within 2 weeks before surgery
Disqualifiers
Patients who received preoperative chemotherapy or radiation therapy
CT images with significant artifacts
Patients with tumor recurrence
Trial population
The investigators collected patients with laryngeal carcinoma from two centers.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
AI
Other interventionRadiomics 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.
Treatment groups
400 Participants
are divided into 3 treatment groupsGroup A: training cohort1 intervention
Group B: internal validation cohort1 intervention
Group C: external validation cohort1 intervention
Trial outcomes
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).
Time frame
Through study completion, an average of 6 monthsSecondary outcomes
1
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.
Time frame
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 yearsSponsors and contacts
Click on the lead sponsor to view all of their trials.
First Affiliated Hospital of Chongqing Medical University
Lead sponsor
Nankai University
Collaborator
Source ClinicalTrials.gov