[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100623789":3},{"organization":4,"armGroups":7,"interventions":15,"overallOfficials":10,"centralContacts":26,"locations":32,"responsibleParty":150,"collaborators":153,"id":161,"slug":162,"hasResults":163,"nctId":164,"briefTitle":165,"officialTitle":166,"acronym":10,"eligibilityCriteria":167,"healthyVolunteers":163,"sex":168,"minAge":169,"maxAge":10,"enrollmentInfo":170,"targetDuration":10,"studyType":173,"phases":10,"briefSummary":174,"conditions":175,"keywords":178,"overallStatus":35,"whyStopped":10,"lastUpdateSubmitDate":184,"lastUpdatePostDateStruct":185,"startDateStruct":188,"completionDateStruct":190,"leadSponsor":192,"locationsCount":193},{"fullName":5,"class":6},"Hebei Medical University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"LAGC Pan-Immunotherapy Cohort",null,"Patients diagnosed with locally advanced gastric cancer (cT3-4a, N+) who are scheduled to receive neoadjuvant chemotherapy combined with PD-1 inhibitors (including but not limited to Sintilimab, Tislelizumab, Camrelizumab, etc.) in a real-world clinical setting. The specific choice of immunotherapy regimen is determined by the treating physician. Multimodal data, including preoperative contrast-enhanced CT images, pathological whole-slide images, and biospecimens (blood\u002Ftissue), will be collected for AI model development and validation.",[13,14],"Drug: Standard of Care PD-1 Inhibitors","Diagnostic Test: Multimodal AI Assessment",[16,21],{"type":17,"name":18,"description":19,"armGroupLabels":20,"otherNames":10},"DRUG","Standard of Care PD-1 Inhibitors","Patients receive standard neoadjuvant chemotherapy (e.g., SOX or XELOX regimen) combined with any NMPA-approved PD-1 inhibitor (including but not limited to Sintilimab, Tislelizumab, Camrelizumab, etc.) as determined by the treating physician in real-world practice.",[9],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":10},"DIAGNOSTIC_TEST","Multimodal AI Assessment","Non-invasive assessment using a multimodal deep learning system (DeepComp) to analyze preoperative contrast-enhanced CT images and pathological slides. The AI model predicts the probability of pathological complete response (pCR) but does not alter the clinical treatment plan.",[9],[27],{"name":28,"role":29,"phone":30,"phoneExt":10,"email":31},"Qun Zhao","CONTACT","+8631186095363","zhaoqun@hebmu.edu.cn",[33,52,65,78,90,104,117,131,143],{"facility":34,"status":35,"city":36,"state":37,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":41,"geoPoint":46,"contacts":47},"The Fifth Affiliated Hospital of Anhui Medical University","RECRUITING","Fuyang","Anhui","050011","China","CN",{"type":42,"coordinates":43},"Point",[44,45],115.81667,32.9,{"lat":45,"lon":44},[48],{"name":49,"role":29,"phone":50,"phoneExt":10,"email":51},"Yanlong Shi","031186095363","yan_long_shi@163.com",{"facility":53,"status":35,"city":54,"state":55,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":56,"geoPoint":60,"contacts":61},"Cangzhou People's Hospital","Cangzhou","Hebei",{"type":42,"coordinates":57},[58,59],116.85334,38.31124,{"lat":59,"lon":58},[62],{"name":63,"role":29,"phone":50,"phoneExt":10,"email":64},"Kaixuan Gao","790806885@qq.com",{"facility":66,"status":35,"city":67,"state":55,"zip":68,"country":39,"countryCode":40,"cosmosGeoPoint":69,"geoPoint":73,"contacts":74},"Hengshui People's Hospital","Hengshui","053099",{"type":42,"coordinates":70},[71,72],115.68348,37.73908,{"lat":72,"lon":71},[75],{"name":76,"role":29,"phone":50,"phoneExt":10,"email":77},"Zhenjiang Guo","guo_zhen_jiang123@163.com",{"facility":79,"status":35,"city":80,"state":55,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":81,"geoPoint":85,"contacts":86},"The Second Affiliated Hospital of Xingtai Medical College","Xingtai",{"type":42,"coordinates":82},[83,84],114.49272,37.06217,{"lat":84,"lon":83},[87],{"name":88,"role":29,"phone":50,"phoneExt":10,"email":89},"Yongli Chen","chen_yong_li888@163.com",{"facility":91,"status":35,"city":92,"state":93,"zip":94,"country":39,"countryCode":40,"cosmosGeoPoint":95,"geoPoint":99,"contacts":100},"Renmin Hospital of Wuhan University","Wuhan","Hubei","430065",{"type":42,"coordinates":96},[97,98],114.26667,30.58333,{"lat":98,"lon":97},[101],{"name":102,"role":29,"phone":50,"phoneExt":10,"email":103},"Lilong Zhang","hb19843362@163.com",{"facility":105,"status":35,"city":106,"state":93,"zip":107,"country":39,"countryCode":40,"cosmosGeoPoint":108,"geoPoint":112,"contacts":113},"Yichang Central Hospital","Yichang","448000",{"type":42,"coordinates":109},[110,111],111.28472,30.71444,{"lat":111,"lon":110},[114],{"name":115,"role":29,"phone":50,"phoneExt":10,"email":116},"Wen Xu","xu_wen_man@163.com",{"facility":118,"status":35,"city":119,"state":120,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":121,"geoPoint":125,"contacts":126},"Baoding Central Hospital","Baoding","None Selected",{"type":42,"coordinates":122},[123,124],115.46246,38.87288,{"lat":124,"lon":123},[127],{"name":128,"role":29,"phone":129,"phoneExt":10,"email":130},"Xiaolong Li","033486223422","hh185496959@126.com",{"facility":132,"status":35,"city":133,"state":120,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":134,"geoPoint":138,"contacts":139},"Shijiazhuang People's Hospital","Shijiazhuang",{"type":42,"coordinates":135},[136,137],114.47861,38.04139,{"lat":137,"lon":136},[140],{"name":141,"role":29,"phone":10,"phoneExt":10,"email":142},"Ning Meng","buezasessiany@outlook.com",{"facility":144,"status":35,"city":133,"state":120,"zip":38,"country":39,"countryCode":40,"cosmosGeoPoint":145,"geoPoint":147,"contacts":148},"the Fourth Hospital of Hebei Medical University",{"type":42,"coordinates":146},[136,137],{"lat":137,"lon":136},[149],{"name":28,"role":29,"phone":50,"phoneExt":10,"email":31},{"type":151,"investigatorFullName":28,"investigatorTitle":152,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"SPONSOR_INVESTIGATOR","Professor",[154,155,157,158,160],{"name":132,"class":6},{"name":118,"class":156},"UNKNOWN",{"name":66,"class":6},{"name":159,"class":156},"Wuhan University Affiliated People's Hospital",{"name":34,"class":156},"100623789","multimodal-ai-for-predicting-response-to-neoadjuvant-immunotherapy-in-gastric-cancer-prism-gc-100623789",false,"NCT07401199","Multimodal AI for Predicting Response to Neoadjuvant Immunotherapy in Gastric Cancer (PRISM-GC)","A Prospective, Multicenter, Real-World Cohort Study for the Development and Validation of a Multimodal Artificial Intelligence System to Predict Response to Neoadjuvant Chemo-Immunotherapy in Locally Advanced Gastric Cancer (The PRISM-GC Study)","Inclusion Criteria:\n\nAge ≥ 18 years.\n\nHistologically confirmed gastric or gastroesophageal junction adenocarcinoma.\n\nClinical stage cT3-4a, N+, M0 (locally advanced) assessed by CT\u002FMRI and endoscopic ultrasound.\n\nScheduled to receive neoadjuvant chemotherapy combined with PD-1 inhibitors (regimens including but not limited to SOX\u002FXELOX + Sintilimab\u002FTislelizumab\u002FCamrelizumab, etc.) as standard of care.\n\nAvailability of standard pre-treatment contrast-enhanced abdominal CT images.\n\nWillingness to provide peripheral blood samples and tumor tissue (biopsy\u002Fsurgical) for sequencing and analysis.\n\nECOG performance status 0-1.\n\nAdequate organ function to tolerate systemic chemotherapy.\n\nExclusion Criteria:\n\nEvidence of distant metastasis (Stage IV) or unresectable disease.\n\nPrevious systemic anti-tumor therapy for gastric cancer (chemotherapy, radiotherapy, or immunotherapy).\n\nHistory of other malignancies within the past 5 years.\n\nActive autoimmune diseases requiring systemic immunosuppressive treatment (contraindication for PD-1 inhibitors).\n\nEmergency surgery due to obstruction, perforation, or uncontrolled bleeding.\n\nSevere metallic artifacts on CT images that interfere with radiomic feature extraction.\n\nPregnancy or lactation.","ALL","18 Years",{"count":171,"type":172},2000,"ESTIMATED","OBSERVATIONAL","Gastric cancer is a major global health challenge. Currently, a combination of chemotherapy and immunotherapy (PD-1 inhibitors) is frequently used before surgery to shrink tumors, a strategy known as neoadjuvant therapy. While this approach is effective for many patients, responses vary significantly, and there are currently no reliable tools to predict which patients will benefit the most before treatment begins.\n\nThe PRISM-GC study aims to develop and validate a novel Artificial Intelligence (AI) system to address this need. This is a prospective, observational study that will collect data from patients diagnosed with locally advanced gastric cancer who are scheduled to receive standard neoadjuvant chemotherapy combined with immunotherapy in a real-world clinical setting. The specific choice of immunotherapy drug is determined by the treating physician and is not dictated by the study.\n\nResearchers will analyze standard preoperative CT scans and pathological tissue slides using advanced deep learning algorithms. The goal is to create a \"multimodal\" AI model that can accurately predict how well a tumor will respond to treatment (specifically, whether the tumor will disappear or shrink significantly). If successful, this AI tool could help doctors personalize treatment plans in the future, ensuring that each patient receives the most effective therapy while avoiding unnecessary side effects.",[176,177],"Gastric Cancer (GC)","Locally Advanced Gastric Cancer",[179,180,181,182,183],"Neoadjuvant Immunotherapy","Artificial Intelligence","Deep Learning","Pathological Complete Response","PD-1 Inhibitors","2026-05-13",{"date":186,"type":187},"2026-05-15","ACTUAL",{"date":189,"type":187},"2026-02-05",{"date":191,"type":172},"2027-12-30",{"name":28,"class":6},9]