[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100645016":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":15,"centralContacts":19,"locations":25,"responsibleParty":48,"collaborators":10,"id":51,"slug":52,"hasResults":53,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":57,"eligibilityCriteria":58,"healthyVolunteers":53,"sex":59,"minAge":60,"maxAge":61,"enrollmentInfo":62,"targetDuration":10,"studyType":65,"phases":10,"briefSummary":66,"conditions":67,"keywords":69,"overallStatus":27,"whyStopped":10,"lastUpdateSubmitDate":76,"lastUpdatePostDateStruct":77,"startDateStruct":80,"completionDateStruct":82,"leadSponsor":84,"locationsCount":85},{"fullName":5,"class":6},"Istanbul Training and Research Hospital","OTHER_GOV",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"Low-Risk Basal Cell Carcinoma",null,"Patients with histopathologically confirmed low-risk basal cell carcinoma, including nodular, superficial, pigmented, adenoid, solid, and nodulocystic subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and subtype prediction.",{"label":13,"type":10,"description":14,"interventionNames":10},"High-Risk Basal Cell Carcinoma","Patients with histopathologically confirmed high-risk basal cell carcinoma, including infiltrative, micronodular, morpheaform, and basosquamous subtypes. Clinical and dermoscopic images will be used for artificial intelligence-based risk classification and histopathological subtype prediction.",[16],{"name":17,"affiliation":5,"role":18},"Ayse Esra Koku Aksu, MD","PRINCIPAL_INVESTIGATOR",[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Tugce Nur Izbudak Kara, MD","CONTACT","+905395976598","eizbudak@icloud.com",[26],{"facility":5,"status":27,"city":28,"state":28,"zip":29,"country":30,"countryCode":10,"cosmosGeoPoint":31,"geoPoint":36,"contacts":37},"RECRUITING","Istanbul","34000","Turkey (Türkiye)",{"type":32,"coordinates":33},"Point",[34,35],28.94966,41.01384,{"lat":35,"lon":34},[38,41,43,44,46],{"name":17,"role":22,"phone":39,"phoneExt":10,"email":40},"+905059126069","esraaksu@gmail.com",{"name":42,"role":22,"phone":23,"phoneExt":10,"email":24},"tugce nur izbudak kara, MD",{"name":17,"role":18,"phone":10,"phoneExt":10,"email":10},{"name":21,"role":45,"phone":10,"phoneExt":10,"email":10},"SUB_INVESTIGATOR",{"name":47,"role":45,"phone":10,"phoneExt":10,"email":10},"Duygu Yamen, MD",{"type":18,"investigatorFullName":49,"investigatorTitle":50,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Tugce Nur Izbudak Kara","MD","100645016","ai-based-risk-classification-and-histopathological-subtype-prediction-of-basal-cell-carcinoma-using-dermoscopic-images-100645016",false,"NCT07677124","AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images","Risk Classification and Prediction of Histopathological Subtypes in Basal Cell Carcinoma Using a CNN-Based Artificial Intelligence Model on Dermoscopic Images","BCC-AI","Inclusion Criteria:\n\n* Patients with histopathologically confirmed basal cell carcinoma.\n* Cases with a specified histopathological subtype.\n* Availability of dermoscopic images with sufficient image quality and resolution for artificial intelligence analysis.\n\nExclusion Criteria:\n\n* Cases without histopathological confirmation of basal cell carcinoma.\n* Cases with unspecified histopathological subtype.\n* Images with insufficient quality or resolution for artificial intelligence analysis.\n* Cases without available dermoscopic images.","ALL","0 Years","100 Years",{"count":63,"type":64},2500,"ESTIMATED","OBSERVATIONAL","This retrospective observational study aims to develop and evaluate a convolutional neural network (CNN)-based artificial intelligence model for risk classification and histopathological subtype prediction of basal cell carcinoma (BCC) using clinical and dermoscopic images. Histopathologically confirmed BCC cases from a dermatology archive will be included. The primary objective is to assess the diagnostic performance of the CNN model in classifying BCC as low-risk or high-risk. Secondary objectives include predicting histopathological subtypes and comparing the model's performance with that of dermatology physicians. Histopathological diagnosis will serve as the reference standard. All archived data will be anonymized before analysis.",[68],"Basal Cell Carcinoma",[68,70,71,72,73,74,75],"Artificial Intelligence","Convolutional Neural Network","Dermoscopy","Skin Cancer","Histopathological Subtypes","Risk Classification","2026-06-29",{"date":78,"type":79},"2026-06-30","ACTUAL",{"date":81,"type":79},"2026-05-22",{"date":83,"type":64},"2027-05-22",{"name":5,"class":6},1]