[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100479458":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":15,"locations":25,"responsibleParty":59,"collaborators":63,"id":70,"slug":71,"hasResults":72,"nctId":73,"briefTitle":74,"officialTitle":74,"acronym":75,"eligibilityCriteria":76,"healthyVolunteers":72,"sex":77,"minAge":78,"maxAge":10,"enrollmentInfo":79,"targetDuration":10,"studyType":82,"phases":10,"briefSummary":83,"conditions":84,"keywords":10,"overallStatus":27,"whyStopped":10,"lastUpdateSubmitDate":86,"lastUpdatePostDateStruct":87,"startDateStruct":90,"completionDateStruct":92,"leadSponsor":94,"locationsCount":95},{"fullName":5,"class":6},"Sixth Affiliated Hospital, Sun Yat-sen University","OTHER",[8,12],{"label":9,"type":10,"description":11,"interventionNames":10},"complete response",null,"Patients receiving neoadjuvant therapy achieved pathological complete response before LARC.",{"label":13,"type":10,"description":14,"interventionNames":10},"non complete response","Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC.",[16,21],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},"Xiaochun Meng","CONTACT","13719166488","mengxch3@mail.sysu.edu.cn",{"name":22,"role":18,"phone":23,"phoneExt":10,"email":24},"Peiyi Xie","13724071514","xiepy6@mail.sysu.edu.cn",[26,40,46,51],{"facility":5,"status":27,"city":28,"state":29,"zip":10,"country":30,"countryCode":31,"cosmosGeoPoint":32,"geoPoint":37,"contacts":38},"RECRUITING","Guangzhou","Guangdong","China","CN",{"type":33,"coordinates":34},"Point",[35,36],113.25,23.11667,{"lat":36,"lon":35},[39],{"name":17,"role":18,"phone":19,"phoneExt":10,"email":20},{"facility":41,"status":42,"city":28,"state":29,"zip":10,"country":30,"countryCode":31,"cosmosGeoPoint":43,"geoPoint":45,"contacts":10},"The First Affiliated Hospital of Jinan University","NOT_YET_RECRUITING",{"type":33,"coordinates":44},[35,36],{"lat":36,"lon":35},{"facility":47,"status":42,"city":28,"state":29,"zip":10,"country":30,"countryCode":31,"cosmosGeoPoint":48,"geoPoint":50,"contacts":10},"The Second Affiliated Hospital of Guangzhou Medical University",{"type":33,"coordinates":49},[35,36],{"lat":36,"lon":35},{"facility":52,"status":42,"city":53,"state":29,"zip":10,"country":30,"countryCode":31,"cosmosGeoPoint":54,"geoPoint":58,"contacts":10},"Fifth Affiliated Hospital, Sun Yat-sen University","Zhuhai",{"type":33,"coordinates":55},[56,57],113.56778,22.27694,{"lat":57,"lon":56},{"type":60,"investigatorFullName":61,"investigatorTitle":62,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","xiaochun meng","The Director of Diagnostic Radiology Department",[64,66,68],{"name":65,"class":6},"Fifth Affiliated Hospital, Sun Yat-Sen University",{"name":67,"class":6},"Second Affiliated Hospital of Guangzhou Medical University",{"name":69,"class":6},"First Affiliated Hospital of Jinan University","100479458","predicting-the-efficacy-of-neoadjuvant-therapy-in-patients-with-locally-advanced-rectal-cancer-using-an-ai-platform-based-on-multi-parametric-mri-100479458",false,"NCT05523245","Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI","DLARC","Inclusion Criteria:\n\n* Clinical suspicion or colonoscopic pathology of rectal cancer\n* Age over 18 years\n* Informed consent and signed informed consent form\n\nExclusion Criteria:\n\n* Poor magnetic resonance image quality, such as severe artifacts\n* Previous treatment for rectal cancer\n* History or combination of other malignant tumours\n* Not Locally Advanced Rectal Cancer (LARC)\n* Not received neoadjuvant therapy or not completed neoadjuvant therapy\n* No surgery\n* Time interval between MRI and surgery was more than 2 weeks\n* Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons","ALL","18 Years",{"count":80,"type":81},1700,"ESTIMATED","OBSERVATIONAL","Establish a deep learning model based on multi-parameter magnetic resonance imaging to predict the efficacy of neoadjuvant therapy for locally advanced rectal cancer.This study intends to combine DCE with conventional MRI images for DL, establish a multi-parameter MRI model for predicting the efficacy of CRT, and compare it with the DL and non-artificial quantitative MRI diagnostic model constructed by conventional MRI to evaluate the role of DL in MRI predicting CRT. And this study also tries to build a DL platform to assess the efficacy of LARC neoadjuvant radiotherapy and chemotherapy, accurately assess patients' complete respose (pCR) after CRT, and provide an important basis for guiding clinical decision-making.",[85],"Rectal Cancer","2026-04-21",{"date":88,"type":89},"2026-04-23","ACTUAL",{"date":91,"type":89},"2022-06-24",{"date":93,"type":81},"2027-12",{"name":5,"class":6},4]