[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100623787":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":12,"centralContacts":17,"locations":27,"responsibleParty":45,"collaborators":10,"id":48,"slug":49,"hasResults":50,"nctId":51,"briefTitle":52,"officialTitle":53,"acronym":10,"eligibilityCriteria":54,"healthyVolunteers":50,"sex":55,"minAge":56,"maxAge":57,"enrollmentInfo":58,"targetDuration":61,"studyType":62,"phases":10,"briefSummary":63,"conditions":64,"keywords":10,"overallStatus":30,"whyStopped":10,"lastUpdateSubmitDate":67,"lastUpdatePostDateStruct":68,"startDateStruct":71,"completionDateStruct":73,"leadSponsor":75,"locationsCount":76},{"fullName":5,"class":6},"Hebei Medical University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"Gastric Cancer Surgery Cohort",null,"Patients diagnosed with gastric cancer who are scheduled to undergo radical gastrectomy (open, laparoscopic, or robotic). All participants will receive standard preoperative contrast-enhanced CT scans. The DeepComp AI model will be applied to these scans to predict the risk of postoperative complications.",[13],{"name":14,"affiliation":15,"role":16},"Qun Zhao","th","PRINCIPAL_INVESTIGATOR",[18,23],{"name":19,"role":20,"phone":21,"phoneExt":10,"email":22},"Ping'an Ding, PhD","CONTACT","+8631186095363","ding_ping_an@hebmu.edu.cn",{"name":24,"role":20,"phone":25,"phoneExt":10,"email":26},"Qun Zhao, PhD","031186095363","zhaoqun@hebmu.edu.cn",[28],{"facility":29,"status":30,"city":31,"state":32,"zip":33,"country":34,"countryCode":35,"cosmosGeoPoint":36,"geoPoint":41,"contacts":42},"the Fourth Hospital of Hebei Medical University","RECRUITING","Shijiazhuang","None Selected","050011","China","CN",{"type":37,"coordinates":38},"Point",[39,40],114.47861,38.04139,{"lat":40,"lon":39},[43],{"name":44,"role":20,"phone":25,"phoneExt":10,"email":22},"Ping'an Ding",{"type":46,"investigatorFullName":14,"investigatorTitle":47,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"SPONSOR_INVESTIGATOR","Professor","100623787","deepcomp-for-prediction-of-gastric-cancer-postoperative-complications-deepcomp-prospective-100623787",false,"NCT07401173","DeepComp for Prediction of Gastric Cancer Postoperative Complications (DeepComp-Prospective)","A Prospective, Multicenter, Observational Study Validating the Multimodal Deep Learning Radiomics Model (DeepComp) for Preoperative Prediction of Major Postoperative Complications in Patients With Gastric Cancer","Inclusion Criteria:\n\nAge ≥ 18 years.\n\nHistologically confirmed gastric adenocarcinoma.\n\nScheduled for elective radical gastrectomy (open, laparoscopic, or robotic) with curative intent.\n\nStandard preoperative contrast-enhanced abdominal CT scans (venous phase) performed within 14 days prior to surgery.\n\nWillingness to sign informed consent.\n\nExclusion Criteria:\n\nEmergency surgery due to perforation, obstruction, or massive bleeding.\n\nIntraoperative findings of distant metastasis (Stage IV) or unresectable disease preventing R0 resection.\n\nConcurrent or previous malignant tumors within the last 5 years (except gastric cancer).\n\nPregnancy or lactation.\n\nSevere metallic artifacts on CT images preventing radiomic analysis.","ALL","18 Years","85 Years",{"count":59,"type":60},500,"ESTIMATED","30 Days","OBSERVATIONAL","Gastric cancer is a leading cause of cancer-related mortality, and radical surgery remains the primary treatment. However, postoperative complications are common and can significantly impact patient recovery and quality of life. Currently, doctors lack precise tools to accurately predict which patients are at high risk for developing severe complications before surgery.\n\nThis study aims to validate a novel artificial intelligence (AI) model called \"DeepComp.\" The DeepComp model integrates clinical data with advanced radiomic features derived from routine preoperative CT scans. Specifically, it analyzes both the tumor characteristics and the patient's body composition (including skeletal muscle and fat distribution) to assess physiological reserve.\n\nIn this prospective, multicenter observational study, researchers will enroll patients scheduled for gastric cancer surgery across five medical centers. The DeepComp model will be used to predict the risk of moderate-to-severe postoperative complications (Clavien-Dindo grade II or higher). These predictions will then be compared with the actual clinical outcomes observed 30 days after surgery. The goal is to determine the accuracy and reliability of the DeepComp model in a real-world clinical setting, potentially providing a powerful tool for personalized surgical risk assessment.",[65,66],"Gastric Cancer (Diagnosis)","Postoperative Complications","2026-04-06",{"date":69,"type":70},"2026-04-09","ACTUAL",{"date":72,"type":70},"2026-03-01",{"date":74,"type":60},"2026-05-01",{"name":14,"class":6},1]