[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100539292":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":10,"centralContacts":31,"locations":37,"responsibleParty":54,"collaborators":57,"id":62,"slug":63,"hasResults":64,"nctId":65,"briefTitle":66,"officialTitle":67,"acronym":68,"eligibilityCriteria":69,"healthyVolunteers":64,"sex":70,"minAge":10,"maxAge":10,"enrollmentInfo":71,"targetDuration":10,"studyType":74,"phases":10,"briefSummary":75,"conditions":76,"keywords":81,"overallStatus":40,"whyStopped":10,"lastUpdateSubmitDate":84,"lastUpdatePostDateStruct":85,"startDateStruct":88,"completionDateStruct":90,"leadSponsor":92,"locationsCount":93},{"fullName":5,"class":6},"Erasmus Medical Center","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Patients with TET",null,"Patients diagnosed with the following TET subtypes:\n\n* Thymoma Type A\n* Thymoma Type AB\n* Thymoma Type B1\n* Thymoma Type B2\n* Thymoma Type B3\n* Thymic Carcinoma",[13],"Diagnostic Test: Artificial Intelligence Diagnostics",{"label":15,"type":10,"description":16,"interventionNames":17},"Recurrence","Patients with thymic epithelial tumors who have experienced recurrence.",[18],"Diagnostic Test: Recurrence Prediction Tool",[20,27],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"DIAGNOSTIC_TEST","Artificial Intelligence Diagnostics","AI Diagnostics uses advanced algorithms for precise histological image analysis to help diagnose disease, including subtype.",[9],[26],"AI Diagnostics, AI Classification",{"type":21,"name":28,"description":29,"armGroupLabels":30,"otherNames":10},"Recurrence Prediction Tool","This AI tool evaluates thymic tumour data and other clinical data and calculates the risk of recurrence, with the aim of analysing whether there is an association with specific subtypes of thymic epithelial tumours and clinical data.",[15],[32],{"name":33,"role":34,"phone":35,"phoneExt":10,"email":36},"Anna Salut Esteve Domínguez","CONTACT","0107043491","a.estevedominguez@erasmusmc.nl",[38],{"facility":39,"status":40,"city":41,"state":42,"zip":43,"country":44,"countryCode":45,"cosmosGeoPoint":46,"geoPoint":51,"contacts":52},"Erasmus MC","RECRUITING","Rotterdam","South Holland","3015 GD","Netherlands","NL",{"type":47,"coordinates":48},"Point",[49,50],4.47917,51.9225,{"lat":50,"lon":49},[53],{"name":33,"role":34,"phone":10,"phoneExt":10,"email":36},{"type":55,"investigatorFullName":33,"investigatorTitle":56,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","PhD student",[58,60],{"name":59,"class":6},"Maastro Clinic, The Netherlands",{"name":61,"class":6},"Hospices Civils de Lyon","100539292","artificial-intelligence-prediction-tool-in-thymic-epithelial-tumors-100539292",false,"NCT06301945","Artificial Intelligence Prediction Tool in Thymic Epithelial Tumors","Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors","INTHYM","Inclusion Criteria:\n\nParticipants with specific diagnoses are eligible for inclusion in the study. The eligible diagnoses include various subtypes of thymoma and thymic carcinoma, specifically:\n\n* Thymoma A\n* Thymoma AB\n* Thymoma B1\n* Thymoma B2\n* Thymoma B3\n* Thymic Carcinoma\n\nInclusion is based on a consensus diagnosis with a level of agreement less than 70%. This criterion is applied during the training phase of the model.\n\nRecurrence Criteria:\n\nParticipants with a documented recurrence outcome within a 5-year period are considered eligible for this aspect of the study. This criterion is primarily applied during the validation phase.","ALL",{"count":72,"type":73},1020,"ESTIMATED","OBSERVATIONAL","Thymic epithelial tumors are rare neoplasms in the anterior mediastinum. The cornerstone of the treatment is surgical resection. Administration of postoperative radiotherapy is usually indicated in patients with more extensive local disease, incomplete resection and\u002For more aggressive subtypes, defined by the WHO histopathological classification.\n\nIn this classification thymoma types A, AB, B1, B2, B3, and thymic carcinoma are distinguished. Studies have shown large discordances between pathologists in subtyping these tumors. Moreover, the WHO classification alone does not accurately predict the risk of recurrence, as within subtypes patients have divergent prognoses.\n\nThe investigators will develop AI models using digital pathology and relevant clinical variables to improve the accuracy of histopathological classification of thymic epithelial tumors, and to better predict the risk of recurrence.\n\nIn this multicentric and international project three existing databases will be used from Rotterdam, Maastricht and Lyon. For all models one database will be used to build AI models, and the other two for external validation.\n\nThe ultimate goal of this project is to develop AI models that support the pathologist in correctly subtyping thymic epithelial tumors, in order to prevent patients from under- or overtreatment with adjuvant radiotherapy.",[77,78,79,80],"Thymic Epithelial Tumor","Thymic Carcinoma","Thymoma","Thymoma and Thymic Carcinoma",[82,83],"Artificial Intelligence","Digital pathology","2024-03-26",{"date":86,"type":87},"2024-03-27","ACTUAL",{"date":89,"type":87},"2023-08-01",{"date":91,"type":73},"2027-08-01",{"name":5,"class":6},1]