[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100643739":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":15,"locations":21,"responsibleParty":37,"collaborators":10,"id":40,"slug":10,"hasResults":41,"nctId":42,"briefTitle":43,"officialTitle":43,"acronym":10,"eligibilityCriteria":44,"healthyVolunteers":45,"sex":46,"minAge":10,"maxAge":10,"enrollmentInfo":47,"targetDuration":10,"studyType":50,"phases":10,"briefSummary":51,"conditions":52,"keywords":10,"overallStatus":54,"whyStopped":10,"lastUpdateSubmitDate":55,"lastUpdatePostDateStruct":56,"startDateStruct":59,"completionDateStruct":61,"leadSponsor":63,"locationsCount":64},{"fullName":5,"class":6},"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","OTHER",[8,11,13],{"label":9,"type":10,"description":10,"interventionNames":10},"Positive Group \u002F Malignant Cohort",null,{"label":12,"type":10,"description":10,"interventionNames":10},"Negative Control Group I \u002F Benign Cohort",{"label":14,"type":10,"description":10,"interventionNames":10},"Negative Control Group II \u002F Healthy Cohort",[16],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},"Lian Yang","CONTACT","18986273791","yanglian@hust.edu.cn",[22],{"facility":23,"status":10,"city":24,"state":25,"zip":26,"country":27,"countryCode":28,"cosmosGeoPoint":29,"geoPoint":34,"contacts":35},"Union Hospital，Tongji Medical College，Huazhong University of Science and Technology","Wuhan","Hubei","430000","China","CN",{"type":30,"coordinates":31},"Point",[32,33],114.26667,30.58333,{"lat":33,"lon":32},[36],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},{"type":38,"investigatorFullName":17,"investigatorTitle":39,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"SPONSOR_INVESTIGATOR","Director","100643739",false,"NCT07639567","Clinical Application Value of Deep Learning-Based \"Opportunistic Screening\" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT","Inclusion Criteria:\n\n1. Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;\n2. Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);\n3. Availability of complete pre-treatment non-contrast CT imaging data.\n\nExclusion Criteria:\n\n1. Non-diagnostic image quality;\n2. Absence of a definitive reference-standard diagnosis;\n3. Incomplete clinical or imaging data.",true,"ALL",{"count":48,"type":49},100000,"ESTIMATED","OBSERVATIONAL","This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.",[53],"Tumor","NOT_YET_RECRUITING","2026-06-05",{"date":57,"type":58},"2026-06-10","ACTUAL",{"date":60,"type":49},"2026-07",{"date":62,"type":49},"2028-07",{"name":17,"class":6},1]