[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100551692":3},{"organization":4,"outcomesModule":7,"designInfo":67,"detailedDescription":11,"studyPopulation":70,"armGroups":71,"interventions":81,"overallOfficials":11,"centralContacts":86,"locations":92,"responsibleParty":108,"collaborators":110,"id":116,"slug":11,"hasResults":117,"nctId":118,"briefTitle":119,"officialTitle":120,"acronym":11,"eligibilityCriteria":121,"healthyVolunteers":117,"sex":122,"minAge":123,"maxAge":11,"enrollmentInfo":124,"targetDuration":11,"studyType":127,"phases":11,"briefSummary":128,"conditions":129,"keywords":132,"overallStatus":94,"whyStopped":11,"lastUpdateSubmitDate":136,"lastUpdatePostDateStruct":137,"startDateStruct":140,"completionDateStruct":142,"leadSponsor":144,"locationsCount":145},{"fullName":5,"class":6},"Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":15,"otherOutcomes":40},[9,13],{"measure":10,"description":11,"timeFrame":12},"Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",null,"Baseline",{"measure":14,"description":11,"timeFrame":12},"Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",[16,18,20,22,24,26,28,30,32,34,36,38],{"measure":17,"description":11,"timeFrame":12},"Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",{"measure":19,"description":11,"timeFrame":12},"Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",{"measure":21,"description":11,"timeFrame":12},"F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",{"measure":23,"description":11,"timeFrame":12},"Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",{"measure":25,"description":11,"timeFrame":12},"Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.",{"measure":27,"description":11,"timeFrame":12},"Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",{"measure":29,"description":11,"timeFrame":12},"Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",{"measure":31,"description":11,"timeFrame":12},"F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",{"measure":33,"description":11,"timeFrame":12},"Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",{"measure":35,"description":11,"timeFrame":12},"Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.",{"measure":37,"description":11,"timeFrame":12},"Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists.",{"measure":39,"description":11,"timeFrame":12},"Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists.",[41,43,45,47,49,51,53,55,57,59,61,63,65],{"measure":42,"description":11,"timeFrame":12},"The number of white blood cells in the peripheral blood.",{"measure":44,"description":11,"timeFrame":12},"The number of neutrophils in the peripheral blood.",{"measure":46,"description":11,"timeFrame":12},"The number of basophils in the peripheral blood.",{"measure":48,"description":11,"timeFrame":12},"The number of eosinophils in the peripheral blood.",{"measure":50,"description":11,"timeFrame":12},"The number of red blood cells in the peripheral blood.",{"measure":52,"description":11,"timeFrame":12},"The concentration of albumin in the peripheral blood.",{"measure":54,"description":11,"timeFrame":12},"The concentration of total protein in the peripheral blood.",{"measure":56,"description":11,"timeFrame":12},"The history of diabetes mellitus.",{"measure":58,"description":11,"timeFrame":12},"The history of hypertension.",{"measure":60,"description":11,"timeFrame":12},"The copy number of Epstein-Barr Virus (EBV) DNA.",{"measure":62,"description":11,"timeFrame":12},"The titer of EBV VCA IgA.",{"measure":64,"description":11,"timeFrame":12},"The titer of EBV EBNA1 IgA.",{"measure":66,"description":11,"timeFrame":12},"The titer of EBV EA IgA.",{"allocation":11,"interventionModel":11,"interventionModelDescription":11,"primaryPurpose":11,"observationalModel":68,"timePerspective":69,"maskingInfo":11},"CASE_CONTROL","CROSS_SECTIONAL","All populations that meet the eligibility criteria.",[72,77],{"label":73,"type":11,"description":74,"interventionNames":75},"Case","Histologically confirmed sbORN that meets the eligibility criteria.",[76],"Other: No Intervention: Observational Cohort",{"label":78,"type":11,"description":79,"interventionNames":80},"Control","Histologically confirmed NPC recurrence that meets the eligibility criteria.",[76],[82],{"type":6,"name":83,"description":84,"armGroupLabels":85,"otherNames":11},"No Intervention: Observational Cohort","No intervention is scheduled for this observational study.",[73,78],[87],{"name":88,"role":89,"phone":90,"phoneExt":11,"email":91},"Xiang-Wei Kong, Ph.D.","CONTACT","0086-020-34071439","kongxw8@mail.sysu.edu.cn",[93],{"facility":5,"status":94,"city":95,"state":96,"zip":97,"country":98,"countryCode":99,"cosmosGeoPoint":100,"geoPoint":105,"contacts":106},"RECRUITING","Guangzhou","Guangdong","510000","China","CN",{"type":101,"coordinates":102},"Point",[103,104],113.25,23.11667,{"lat":104,"lon":103},[107],{"name":88,"role":89,"phone":90,"phoneExt":11,"email":91},{"type":109,"investigatorFullName":11,"investigatorTitle":11,"investigatorAffiliation":11,"oldNameTitle":11,"oldOrganization":11},"SPONSOR",[111,113],{"name":112,"class":6},"Sun Yat-sen University",{"name":114,"class":115},"Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China","UNKNOWN","100551692",false,"NCT06463392","Deep Learning-based sbORN Diagnostic Model","Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model","Inclusion Criteria:\n\n* Equal to or older than 18 years old.\n* A history of histologically confirmed nonkeratinizing undifferentiated nasopharyngeal carcinoma.\n* A history of radical radiotherapy at nasopharynx.\n* Complete remission six months post radical radiotherapy according to RECIST 1.1.\n* No evidence of distant metastasis upon recruitment.\n* Diagnosis of sbORN given by senior radiologist with 2-4 Likert scores.\n* Consent to biopsy awake or under general anesthesia.\n* Consent to perform blood tests, EBV DNA, EBV IgAs, and MRI inspection of nasopharynx and neck.\n* With a written consent.\n\nExclusion Criteria:\n\n* MRI artifacts or other factors that interfere radiological diagnosis and region of interest contouring.\n* Suspected lesion is not confined to nasopharynx and skull-base.","ALL","18 Years",{"count":125,"type":126},312,"ESTIMATED","OBSERVATIONAL","Skull-base osteonecrosis (sbORN) is a severe long-term complication of nasopharyngeal carcinoma (NPC) post radiotherapy, which significantly diminish the quality of life, increase the risk of internal carotid artery rupture, and is frequently misdiagnosed as NPC recurrence. Novel diagnostic tools are therefore clinically significant. In this study, the investigators seek to ask if a deep-learning-based model shows a significantly higher sensitivity than radiologists. With a cross-sectional design, the investigators aim to recruit 312 participants in Sun Yat-sen Memorial Hospital, Guangzhou, China that meet the eligibility criteria.",[130,131],"Nasopharyngeal Carcinoma","Herpesvirus 4, Human",[130,133,134,135],"Skull-base Osteonecrosis","Radiotherapy","Epstein-Barr Virus","2024-09-27",{"date":138,"type":139},"2024-10-01","ACTUAL",{"date":141,"type":139},"2024-07-01",{"date":143,"type":126},"2030-12-31",{"name":5,"class":6},1]