[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100582079":3},{"organization":4,"armGroups":7,"interventions":8,"overallOfficials":7,"centralContacts":7,"locations":7,"responsibleParty":13,"collaborators":7,"id":17,"slug":7,"hasResults":18,"nctId":19,"briefTitle":20,"officialTitle":20,"acronym":21,"eligibilityCriteria":22,"healthyVolunteers":18,"sex":23,"minAge":24,"maxAge":25,"enrollmentInfo":26,"targetDuration":29,"studyType":30,"phases":7,"briefSummary":31,"conditions":32,"keywords":7,"overallStatus":34,"whyStopped":7,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":7},{"fullName":5,"class":6},"Hebei Medical University","OTHER",null,[9],{"type":10,"name":11,"description":12,"armGroupLabels":7,"otherNames":7},"DIAGNOSTIC_TEST","BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction","This intervention utilizes a deep learning model that integrates BUB1 gene expression, radiopathomics (quantitative imaging features), and histopathological data to predict peritoneal metastasis and cachexia in gastric cancer (GC) patients. Unlike traditional approaches, this model combines genomic, imaging, and pathological data to enhance early detection and improve prognostic accuracy. The model aims to identify key patterns in multi-modal data to offer personalized predictions for GC progression. By leveraging artificial intelligence, it seeks to support clinicians in decision-making, improving patient outcomes through earlier interventions and tailored treatments. This approach offers a novel, comprehensive method for predicting GC metastasis and cachexia, providing a unique tool compared to existing interventions.",{"type":14,"investigatorFullName":15,"investigatorTitle":16,"investigatorAffiliation":5,"oldNameTitle":7,"oldOrganization":7},"SPONSOR_INVESTIGATOR","Qun Zhao","Professor","100582079",false,"NCT06858644","Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning","BUDDLE","Inclusion Criteria:\n\nAdults aged 18-75 years diagnosed with gastric cancer (GC) at any stage. Histopathologically confirmed GC with available radiological (CT\u002FMRI) and pathological data (biopsy samples).\n\nPatients with or at risk of peritoneal metastasis and\u002For cachexia, as determined by clinical assessment and imaging.\n\nAbility to provide informed consent and comply with study protocols. Willingness to undergo regular follow-up imaging and clinical evaluation for the duration of the study.\n\nExclusion Criteria:\n\nPatients with other primary cancers or serious comorbidities (e.g., severe cardiovascular disease, uncontrolled diabetes).\n\nPregnant or breastfeeding women. Patients with contraindications to MRI or CT imaging. Those with insufficient clinical data (e.g., missing radiopathological information) for model training.\n\nPatients who are unable or unwilling to comply with the study protocol, including follow-up visits and evaluations.","ALL","18 Years","75 Years",{"count":27,"type":28},500,"ESTIMATED","5 Years","OBSERVATIONAL","This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.",[33],"Gastric (Stomach) Cancer","NOT_YET_RECRUITING","2025-02-27",{"date":37,"type":38},"2025-03-05","ACTUAL",{"date":40,"type":28},"2025-03-01",{"date":42,"type":28},"2027-03-01",{"name":15,"class":6}]