[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100581524":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":15,"centralContacts":19,"locations":25,"responsibleParty":51,"collaborators":10,"id":54,"slug":55,"hasResults":56,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":10,"eligibilityCriteria":60,"healthyVolunteers":61,"sex":62,"minAge":63,"maxAge":10,"enrollmentInfo":64,"targetDuration":10,"studyType":67,"phases":10,"briefSummary":68,"conditions":69,"keywords":10,"overallStatus":41,"whyStopped":10,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":75,"completionDateStruct":77,"leadSponsor":79,"locationsCount":80},{"fullName":5,"class":6},"Chang Gung Memorial Hospital","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"control group",null,"The control group included both benign ovarian tumors and an enriched dataset.",{"label":13,"type":10,"description":14,"interventionNames":10},"case group","ovarian cancer",[16],{"name":17,"affiliation":5,"role":18},"Gigin Lin, MD, PhD","PRINCIPAL_INVESTIGATOR",[20],{"name":17,"role":21,"phone":22,"phoneExt":23,"email":24},"CONTACT","886-3-3281200","2575","giginlin@cgmh.org.tw",[26,39],{"facility":5,"status":27,"city":28,"state":29,"zip":30,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":10},"COMPLETED","Taoyuan City","Guishan District","333","Taiwan","TW",{"type":34,"coordinates":35},"Point",[36,37],121.29696,24.99368,{"lat":37,"lon":36},{"facility":40,"status":41,"city":42,"state":43,"zip":30,"country":31,"countryCode":32,"cosmosGeoPoint":44,"geoPoint":46,"contacts":47},"Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital","RECRUITING","Taoyuan","Guishan",{"type":34,"coordinates":45},[36,37],{"lat":37,"lon":36},[48,50],{"name":17,"role":21,"phone":49,"phoneExt":23,"email":24},"886-3281200",{"name":17,"role":18,"phone":10,"phoneExt":10,"email":10},{"type":18,"investigatorFullName":52,"investigatorTitle":53,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Gigin Lin","Clinical Professor","100581524","ovarian-cancer-identification-on-ct-using-deep-learning-100581524",false,"NCT06851429","Ovarian Cancer Identification on CT Using Deep Learning","Development and Validation of a Deep Learning Model for Ovarian Cancer Identification on CT: A Nationwide Population-Based and International Study","Inclusion Criteria:\n\n1. Age ≥ 20 years old.\n2. Female\n3. undergone a CT scan\n4. undergone a CT scan within 180 days prior to ovarian surgery for histopathological evaluation.\n\nExclusion Criteria:\n\n1. Age \\\u003C 20 years old.\n2. Non-female\n3. Non-CT imaging\n4. Incorrect image orientation\n5. Number of slices \\\u003C 10\n6. Slice thickness \\>10 mm or \\\u003C 1 mm\n7. Unsuccessful DICM-to-NIfTI\n8. Pelvic subvolume extraction failed\n9. Non-contrast CT scans\n10. Metallic artifacts\n11. Inconclusive cases",true,"FEMALE","20 Years",{"count":65,"type":66},12578,"ESTIMATED","OBSERVATIONAL","Ovarian cancer remains the deadliest gynecologic malignancy, with poor survival rates largely due to late-stage diagnosis. Early detection is crucial, yet no universally accepted screening method exists. Current imaging techniques and biomarkers, such as CA-125, have limitations in specificity and sensitivity. This study aims to develop and evaluate a deep learning-based computer-aided diagnosis tool (CAT-OV), for ovarian cancer detection using CT imaging. The system integrates a Body Part Regression (BPR) model for pelvic localization and a Multiple Instance Learning (MIL) ensemble classifier for cancer prediction. The model was trained and validated using retrospective datasets from Taiwan, the United States, and a nationwide real-world cohort. Stringent preprocessing and quality control measures were implemented to enhance model accuracy. Results highlight the potential of AI-driven CT screening in improving early detection, though further validation is needed for clinical adoption.",[70],"Ovarian Cancer","2026-08-10",{"date":73,"type":74},"2026-08-12","ACTUAL",{"date":76,"type":74},"2022-09-01",{"date":78,"type":66},"2028-08-31",{"name":5,"class":6},2]