[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100551720":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":10,"centralContacts":10,"locations":28,"responsibleParty":47,"collaborators":51,"id":54,"slug":55,"hasResults":56,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":10,"eligibilityCriteria":60,"healthyVolunteers":56,"sex":61,"minAge":62,"maxAge":63,"enrollmentInfo":64,"targetDuration":10,"studyType":67,"phases":10,"briefSummary":68,"conditions":69,"keywords":72,"overallStatus":31,"whyStopped":10,"lastUpdateSubmitDate":73,"lastUpdatePostDateStruct":74,"startDateStruct":77,"completionDateStruct":79,"leadSponsor":81,"locationsCount":82},{"fullName":5,"class":6},"First Affiliated Hospital of Chongqing Medical University","OTHER",[8,14,17],{"label":9,"type":10,"description":11,"interventionNames":12},"training cohort",null,"No interventions",[13],"Other: AI",{"label":15,"type":10,"description":11,"interventionNames":16},"internal validation cohort",[13],{"label":18,"type":10,"description":11,"interventionNames":19},"external validation cohort",[13],[21],{"type":6,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"AI","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.",[18,15,9],[26,27],"radiomics","deep learning",[29],{"facility":30,"status":31,"city":32,"state":10,"zip":10,"country":33,"countryCode":34,"cosmosGeoPoint":35,"geoPoint":40,"contacts":41},"The First Affiliated Hospital of Chongqing Medical University","RECRUITING","Chongqing","China","CN",{"type":36,"coordinates":37},"Point",[38,39],106.55771,29.56026,{"lat":39,"lon":38},[42],{"name":43,"role":44,"phone":45,"phoneExt":10,"email":46},"Peng juan","CONTACT","+86 189 8328 0171","pengjuan1209@126.com",{"type":48,"investigatorFullName":49,"investigatorTitle":50,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","xinwei Chen","Radiology Department",[52],{"name":53,"class":6},"Nankai University","100551720","ai-models-to-predict-thyroid-cartilage-invasion-in-laryngeal-carcinoma-100551720",false,"NCT06463756","AI Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma","CT-based Radiomics, Two-dimensional and Three-dimensional Deep Learning Models to Predict Thyroid Cartilage Invasion in Laryngeal Carcinoma: a Multicenter Study","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","ALL","18 Years","81 Years",{"count":65,"type":66},400,"ESTIMATED","OBSERVATIONAL","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.",[70,71],"Laryngeal Carcinoma","Thyroid Cartilage",[26,27],"2024-08-20",{"date":75,"type":76},"2024-08-22","ACTUAL",{"date":78,"type":76},"2023-08-13",{"date":80,"type":66},"2024-10-13",{"name":5,"class":6},1]