Ignite Creation Date:
2025-12-24 @ 11:30 PM
Ignite Modification Date:
2025-12-25 @ 9:17 PM
Study NCT ID:
NCT06463756
Status:
RECRUITING
Last Update Posted:
2024-08-22
First Post:
2024-06-12
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007822', 'term': 'Laryngeal Neoplasms'}], 'ancestors': [{'id': 'D010039', 'term': 'Otorhinolaryngologic Neoplasms'}, {'id': 'D006258', 'term': 'Head and Neck Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D007818', 'term': 'Laryngeal Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D010038', 'term': 'Otorhinolaryngologic Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D000077321', 'term': 'Deep Learning'}], 'ancestors': [{'id': 'D000069550', 'term': 'Machine Learning'}, {'id': 'D001185', 'term': 'Artificial Intelligence'}, {'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}, {'id': 'D016571', 'term': 'Neural Networks, Computer'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-08-13', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-08', 'completionDateStruct': {'date': '2024-10-13', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-08-20', 'studyFirstSubmitDate': '2024-06-12', 'studyFirstSubmitQcDate': '2024-06-17', 'lastUpdatePostDateStruct': {'date': '2024-08-22', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-06-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-09-13', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area under the curve, AUC', 'timeFrame': 'Through study completion, an average of 6 months', 'description': '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).'}], 'secondaryOutcomes': [{'measure': 'Disease-Free-Survival, DFS', 'timeFrame': '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', 'description': '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.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['radiomics', 'deep learning'], 'conditions': ['Laryngeal Carcinoma', 'Thyroid Cartilage']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '81 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'The investigators collected patients with laryngeal carcinoma from two centers.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Availability of complete clinical data\n2. Surgery-proven or biopsy-proven diagnosis of laryngeal squamous cell carcinoma\n3. CT examination performed within 2 weeks before surgery\n\nExclusion Criteria:\n\n1. Patients who received preoperative chemotherapy or radiation therapy\n2. CT images with significant artifacts\n3. Patients with tumor recurrence'}, 'identificationModule': {'nctId': 'NCT06463756', 'briefTitle': 'AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma', 'organization': {'class': 'OTHER', 'fullName': 'First Affiliated Hospital of Chongqing Medical University'}, 'officialTitle': 'CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study', 'orgStudyIdInfo': {'id': '2024-Chenx'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'training cohort', 'description': 'No interventions', 'interventionNames': ['Other: AI']}, {'label': 'internal validation cohort', 'description': 'No interventions', 'interventionNames': ['Other: AI']}, {'label': 'external validation cohort', 'description': 'No interventions', 'interventionNames': ['Other: AI']}], 'interventions': [{'name': 'AI', 'type': 'OTHER', 'otherNames': ['radiomics', 'deep learning'], 'description': '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.\n\nDeep 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.', 'armGroupLabels': ['external validation cohort', 'internal validation cohort', 'training cohort']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Chongqing', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Peng juan', 'role': 'CONTACT', 'email': 'pengjuan1209@126.com', 'phone': '+86 189 8328 0171'}], 'facility': 'The First Affiliated Hospital of Chongqing Medical University', 'geoPoint': {'lat': 29.56026, 'lon': 106.55771}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The clinical data are manually collected from the clinical case system; the CT image data are exported from the PACS system and anonymously stored on a separate data disk; and the image materials are collected and anonymously stored on a separate data disk.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'First Affiliated Hospital of Chongqing Medical University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Nankai University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Radiology Department', 'investigatorFullName': 'xinwei Chen', 'investigatorAffiliation': 'First Affiliated Hospital of Chongqing Medical University'}}}}