[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100599735":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":19,"centralContacts":23,"locations":31,"responsibleParty":49,"collaborators":10,"id":52,"slug":53,"hasResults":54,"nctId":55,"briefTitle":56,"officialTitle":56,"acronym":57,"eligibilityCriteria":58,"healthyVolunteers":54,"sex":59,"minAge":60,"maxAge":10,"enrollmentInfo":61,"targetDuration":10,"studyType":64,"phases":10,"briefSummary":65,"conditions":66,"keywords":71,"overallStatus":34,"whyStopped":10,"lastUpdateSubmitDate":72,"lastUpdatePostDateStruct":73,"startDateStruct":76,"completionDateStruct":78,"leadSponsor":80,"locationsCount":81},{"fullName":5,"class":6},"Tongji Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery",null,"Patients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery.",[13],"Diagnostic Test: The high-throughput extraction of large amounts of quantitative image features from medical images",[15],{"type":16,"name":17,"description":17,"armGroupLabels":18,"otherNames":10},"DIAGNOSTIC_TEST","The high-throughput extraction of large amounts of quantitative image features from medical images",[9],[20],{"name":21,"affiliation":5,"role":22},"Yangkai Li, MD, PhD","PRINCIPAL_INVESTIGATOR",[24,28],{"name":21,"role":25,"phone":26,"phoneExt":10,"email":27},"CONTACT","+8613995516396","doclyk@163.com",{"name":29,"role":25,"phone":10,"phoneExt":10,"email":30},"Lin Zhou, MSc","zhoul0928@163.com",[32],{"facility":33,"status":34,"city":35,"state":36,"zip":37,"country":38,"countryCode":39,"cosmosGeoPoint":40,"geoPoint":45,"contacts":46},"Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","RECRUITING","Wuhan","Hubei","430030","China","CN",{"type":41,"coordinates":42},"Point",[43,44],114.26667,30.58333,{"lat":44,"lon":43},[47,48],{"name":21,"role":25,"phone":26,"phoneExt":10,"email":27},{"name":29,"role":25,"phone":10,"phoneExt":10,"email":30},{"type":22,"investigatorFullName":50,"investigatorTitle":51,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Yangkai Li","professor","100599735","deep-learning-model-predicts-pathological-complete-response-of-esophageal-squamous-cell-carcinoma-following-neoadjuvant-immunochemotherapy-100599735",false,"NCT07088354","Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy","DL-ESCC","Inclusion Criteria:\n\n1. Pathologically confirmed esophageal squamous cell carcinoma (ESCC).\n2. Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.\n3. Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.\n4. Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.\n\nExclusion Criteria:\n\n1. Diagnosis of other malignancies.\n2. Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.\n3. Incomplete clinical data.\n4. Poor-quality CT imaging.","ALL","18 Years",{"count":62,"type":63},300,"ESTIMATED","OBSERVATIONAL","This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.",[67,68,69,70],"Esophageal Squamous Cell Carcinoma","Neoadjuvant Immunochemotherapy","Pathological Complete Response","Deep Learning",[67,68,70,69],"2025-07-24",{"date":74,"type":75},"2025-07-28","ACTUAL",{"date":77,"type":75},"2025-03-01",{"date":79,"type":63},"2026-12-01",{"name":5,"class":6},1]