[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100572580":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":23,"studyPopulation":13,"armGroups":24,"interventions":35,"overallOfficials":42,"centralContacts":47,"locations":57,"responsibleParty":75,"collaborators":13,"id":77,"slug":13,"hasResults":78,"nctId":79,"briefTitle":80,"officialTitle":80,"acronym":13,"eligibilityCriteria":81,"healthyVolunteers":82,"sex":83,"minAge":84,"maxAge":85,"enrollmentInfo":86,"targetDuration":13,"studyType":89,"phases":90,"briefSummary":92,"conditions":93,"keywords":13,"overallStatus":59,"whyStopped":13,"lastUpdateSubmitDate":95,"lastUpdatePostDateStruct":96,"startDateStruct":99,"completionDateStruct":101,"leadSponsor":103,"locationsCount":104},{"fullName":5,"class":6},"Yunnan Cancer Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"Extract the whole liver fat fraction","Extract the whole liver fat fraction using the threshold extraction method.","one year",null,{"allocation":15,"interventionModel":16,"interventionModelDescription":13,"primaryPurpose":17,"observationalModel":13,"timePerspective":13,"maskingInfo":18},"RANDOMIZED","SINGLE_GROUP","DIAGNOSTIC",{"masking":19,"maskingDescription":13,"whoMasked":20},"DOUBLE",[21,22],"PARTICIPANT","OUTCOMES_ASSESSOR","Regarding the extraction of liver fat fraction, the traditional axial ROI method involves selecting several regions of interest (ROIs) at the largest cross-sectional level or across multiple continuous sections, and taking the average value as the whole-liver fat fraction. This method is complex, time-consuming, and cannot obtain the whole-liver fat fraction. In this study, a threshold extraction method is used to obtain the whole-liver fat fraction, enabling a 2D-to-3D conversion, which is more time-efficient and labor-saving, and provides a more accurate measurement.",[25,31],{"label":26,"type":27,"description":28,"interventionNames":29},"neoadjuvant chemotherapy group","EXPERIMENTAL","Cancer patients undergoing chemotherapy.",[30],"Drug: Neoadjuvant chemotherapy",{"label":32,"type":33,"description":34,"interventionNames":13},"Non-neoadjuvant chemotherapy group","NO_INTERVENTION","Cancer patients not undergoing chemotherapy.",[36],{"type":37,"name":38,"description":38,"armGroupLabels":39,"otherNames":40},"DRUG","Neoadjuvant chemotherapy",[26],[41],"Non",[43],{"name":44,"affiliation":45,"role":46},"Lianhua Ye","Ethics Committee of Yunnan Provincial Cancer Hospital","STUDY_DIRECTOR",[48,53],{"name":49,"role":50,"phone":51,"phoneExt":13,"email":52},"Lizhu Liu, Graduate","CONTACT","18287509587","liulizhu2022@163.com",{"name":54,"role":50,"phone":55,"phoneExt":13,"email":56},"Zhenhui Li, MD","13698736132","lizhenhui@kmmu.edu.cn",[58],{"facility":5,"status":59,"city":60,"state":61,"zip":62,"country":63,"countryCode":64,"cosmosGeoPoint":65,"geoPoint":70,"contacts":71},"RECRUITING","Kunming","Yunnan","650118","China","CN",{"type":66,"coordinates":67},"Point",[68,69],102.71833,25.03889,{"lat":69,"lon":68},[72],{"name":73,"role":50,"phone":74,"phoneExt":13,"email":13},"Guojun Zhang, Professor","0871-68173640",{"type":76,"investigatorFullName":13,"investigatorTitle":13,"investigatorAffiliation":13,"oldNameTitle":13,"oldOrganization":13},"SPONSOR","100572580",false,"NCT06735118","Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver","Inclusion Criteria:\n\n1. CT\u002FB ultrasound showed no fatty liver\n2. No MRI contraindications, including pacemaker, stent, metal implant, or claustrophobia\n3. Received neoadjuvant\u002Fadjuvant chemotherapy\n\nExclusion Criteria:\n\n1. Missing follow-up information\n2. Liver lesions (metastases, hemangioma, etc.)\n3. Poor image quality",true,"ALL","18 Years","80 Years",{"count":87,"type":88},120,"ESTIMATED","INTERVENTIONAL",[91],"NA","The purpose of this study is to quantitatively assess the changes in liver fat content in cancer patients before and after treatment.\n\nThe main questions it aims to answer are:How does the liver fat fraction change before and after chemotherapy? In this study, patients undergoing mDixon Quant scanning are subjected to fully automated segmentation and measurement of liver fat content using artificial intelligence.",[94],"Non-Alcoholic Fatty Liver Disease","2024-12-13",{"date":97,"type":98},"2024-12-16","ACTUAL",{"date":100,"type":98},"2023-12-25",{"date":102,"type":88},"2024-12-30",{"name":5,"class":6},1]