[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100479458":3},{"organization":4,"outcomesModule":7,"designInfo":26,"detailedDescription":25,"studyPopulation":29,"armGroups":30,"interventions":25,"overallOfficials":25,"centralContacts":37,"locations":47,"responsibleParty":81,"collaborators":85,"id":92,"slug":25,"hasResults":93,"nctId":94,"briefTitle":95,"officialTitle":95,"acronym":96,"eligibilityCriteria":97,"healthyVolunteers":93,"sex":98,"minAge":99,"maxAge":25,"enrollmentInfo":100,"targetDuration":25,"studyType":103,"phases":25,"briefSummary":104,"conditions":105,"keywords":25,"overallStatus":49,"whyStopped":25,"lastUpdateSubmitDate":107,"lastUpdatePostDateStruct":108,"startDateStruct":111,"completionDateStruct":113,"leadSponsor":115,"locationsCount":116},{"fullName":5,"class":6},"Sixth Affiliated Hospital, Sun Yat-sen University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":25},[9],{"measure":10,"description":11,"timeFrame":12},"The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response","The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.","baseline and pre-operation",[14,17,19,22],{"measure":15,"description":16,"timeFrame":12},"The specificity of models in prediction tumor response","The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.",{"measure":18,"description":16,"timeFrame":12},"The sensitivity of models in prediction tumor response",{"measure":20,"description":21,"timeFrame":12},"The positive predictive value of models in prediction tumor response","The positive predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.",{"measure":23,"description":24,"timeFrame":12},"The negative predictive value of models in prediction tumor response","The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.",null,{"allocation":25,"interventionModel":25,"interventionModelDescription":25,"primaryPurpose":25,"observationalModel":27,"timePerspective":28,"maskingInfo":25},"COHORT","PROSPECTIVE","Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery",[31,34],{"label":32,"type":25,"description":33,"interventionNames":25},"complete response","Patients receiving neoadjuvant therapy achieved pathological complete response before LARC.",{"label":35,"type":25,"description":36,"interventionNames":25},"non complete response","Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC.",[38,43],{"name":39,"role":40,"phone":41,"phoneExt":25,"email":42},"Xiaochun Meng","CONTACT","13719166488","mengxch3@mail.sysu.edu.cn",{"name":44,"role":40,"phone":45,"phoneExt":25,"email":46},"Peiyi Xie","13724071514","xiepy6@mail.sysu.edu.cn",[48,62,68,73],{"facility":5,"status":49,"city":50,"state":51,"zip":25,"country":52,"countryCode":53,"cosmosGeoPoint":54,"geoPoint":59,"contacts":60},"RECRUITING","Guangzhou","Guangdong","China","CN",{"type":55,"coordinates":56},"Point",[57,58],113.25,23.11667,{"lat":58,"lon":57},[61],{"name":39,"role":40,"phone":41,"phoneExt":25,"email":42},{"facility":63,"status":64,"city":50,"state":51,"zip":25,"country":52,"countryCode":53,"cosmosGeoPoint":65,"geoPoint":67,"contacts":25},"The First Affiliated Hospital of Jinan University","NOT_YET_RECRUITING",{"type":55,"coordinates":66},[57,58],{"lat":58,"lon":57},{"facility":69,"status":64,"city":50,"state":51,"zip":25,"country":52,"countryCode":53,"cosmosGeoPoint":70,"geoPoint":72,"contacts":25},"The Second Affiliated Hospital of Guangzhou Medical University",{"type":55,"coordinates":71},[57,58],{"lat":58,"lon":57},{"facility":74,"status":64,"city":75,"state":51,"zip":25,"country":52,"countryCode":53,"cosmosGeoPoint":76,"geoPoint":80,"contacts":25},"Fifth Affiliated Hospital, Sun Yat-sen University","Zhuhai",{"type":55,"coordinates":77},[78,79],113.56778,22.27694,{"lat":79,"lon":78},{"type":82,"investigatorFullName":83,"investigatorTitle":84,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"PRINCIPAL_INVESTIGATOR","xiaochun meng","The Director of Diagnostic Radiology Department",[86,88,90],{"name":87,"class":6},"Fifth Affiliated Hospital, Sun Yat-Sen University",{"name":89,"class":6},"Second Affiliated Hospital of Guangzhou Medical University",{"name":91,"class":6},"First Affiliated Hospital of Jinan University","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":101,"type":102},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.",[106],"Rectal Cancer","2026-04-21",{"date":109,"type":110},"2026-04-23","ACTUAL",{"date":112,"type":110},"2022-06-24",{"date":114,"type":102},"2027-12",{"name":5,"class":6},4]