[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100612189":3},{"organization":4,"armGroups":7,"interventions":22,"overallOfficials":28,"centralContacts":32,"locations":42,"responsibleParty":61,"collaborators":63,"id":70,"slug":71,"hasResults":72,"nctId":73,"briefTitle":74,"officialTitle":75,"acronym":10,"eligibilityCriteria":76,"healthyVolunteers":72,"sex":77,"minAge":78,"maxAge":79,"enrollmentInfo":80,"targetDuration":10,"studyType":83,"phases":10,"briefSummary":84,"conditions":85,"keywords":88,"overallStatus":45,"whyStopped":10,"lastUpdateSubmitDate":94,"lastUpdatePostDateStruct":95,"startDateStruct":98,"completionDateStruct":100,"leadSponsor":102,"locationsCount":103},{"fullName":5,"class":6},"The First Affiliated Hospital with Nanjing Medical University","OTHER",[8,14,18],{"label":9,"type":10,"description":11,"interventionNames":12},"Cohort 1 (Internal Derivation Cohort)",null,"Retrospective case-only cohort of adults with pathologically confirmed gastric cancer who underwent preoperative contrast-enhanced CT at the sponsoring institution. Existing CT images and clinical\u002Fpathology records will be used to train and test the AI model and to estimate diagnostic performance for T and N staging.",[13],"Diagnostic Test: CT scan",{"label":15,"type":10,"description":16,"interventionNames":17},"Cohort 2 (External Validation Cohort A)","Independent retrospective case-only cohort from an external hospital with the same inclusion\u002Fexclusion criteria. Used solely for external validation to assess reproducibility across sites and scanners.",[13],{"label":19,"type":10,"description":20,"interventionNames":21},"Cohort 3 (External Validation Cohort B)","A second independent retrospective validation cohort from another institution to further test generalizability.",[13],[23],{"type":24,"name":25,"description":26,"armGroupLabels":27,"otherNames":10},"DIAGNOSTIC_TEST","CT scan","preoperative contrast-enhanced CT",[9,15,19],[29],{"name":30,"affiliation":5,"role":31},"Zhang Yudong","PRINCIPAL_INVESTIGATOR",[33,38],{"name":34,"role":35,"phone":36,"phoneExt":10,"email":37},"Zhang Yudong, PHD, MD","CONTACT","+8618251966069","zhangyd3895@njmu.edu.cn",{"name":39,"role":35,"phone":40,"phoneExt":10,"email":41},"Qiong Li","+8618351977281","njmu_lq@163.com",[43],{"facility":44,"status":45,"city":46,"state":47,"zip":10,"country":48,"countryCode":49,"cosmosGeoPoint":50,"geoPoint":55,"contacts":56},"The First Affiliated Hospital of Nanjing Medical University","RECRUITING","Nanjing","Jiangsu","China","CN",{"type":51,"coordinates":52},"Point",[53,54],118.77778,32.06167,{"lat":54,"lon":53},[57],{"name":58,"role":35,"phone":59,"phoneExt":10,"email":60},"Yue Wang","025-68306222","jsphkjwy@163.com",{"type":62,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[64,66,68],{"name":65,"class":6},"Jiangsu Cancer Institute & Hospital",{"name":67,"class":6},"Zhengzhou University",{"name":69,"class":6},"Peking University First Hospital","100612189","ai-assisted-detection-and-staging-of-gastric-cancer-using-contrast-enhanced-ct-100612189",false,"NCT07250347","AI-Assisted Detection and Staging of Gastric Cancer Using Contrast-Enhanced CT","Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study","Inclusion Criteria:\n\n1. pathologically confirmed gastric cancer;\n2. preoperative contrast-enhanced CT performed;\n3. no evidence of distant metastasis on baseline staging;\n4. curative-intent management with complete postoperative histopathology.\n\nExclusion Criteria:\n\n1. prior treatment before surgery;\n2. non-diagnostic or poor-quality CT precluding evaluation.","ALL","18 Years","85 Years",{"count":81,"type":82},8000,"ESTIMATED","OBSERVATIONAL","Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Contrast-enhanced CT (CECT) is guideline-endorsed for initial staging, yet performance varies across institutions and readers. This study will evaluate an artificial-intelligence (AI) system that analyzes routine CECT to detect gastric cancer and assign four-class T stage (T1-T4) and N stage (N0-N3) .",[86,87],"Gastric Cancer Stage","Gastric Cancer Patients Undergoing Gastrectomy",[89,90,91,92,93],"Gastric cancer","stage","artificial-intelligence","detection","contrast-enhanced CT","2025-11-24",{"date":96,"type":97},"2025-11-26","ACTUAL",{"date":99,"type":97},"2025-08-01",{"date":101,"type":82},"2028-12-30",{"name":5,"class":6},1]