[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656831":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":17,"studyPopulation":18,"armGroups":19,"interventions":25,"overallOfficials":31,"centralContacts":36,"locations":42,"responsibleParty":59,"collaborators":62,"id":66,"slug":67,"hasResults":68,"nctId":69,"briefTitle":70,"officialTitle":71,"acronym":13,"eligibilityCriteria":72,"healthyVolunteers":68,"sex":73,"minAge":74,"maxAge":75,"enrollmentInfo":76,"targetDuration":13,"studyType":79,"phases":13,"briefSummary":80,"conditions":81,"keywords":85,"overallStatus":44,"whyStopped":13,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":99},{"fullName":5,"class":6},"Anhui Provincial Hospital","OTHER_GOV",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"Recurrence-Free Survival (RFS)","Recurrence-free survival (RFS) is defined as the time from the date of surgical resection to the date of first tumor recurrence (local, regional, or distant) or death from any cause, whichever occurs first.","Up to 5 years post-surgery",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"COHORT","RETROSPECTIVE","This study is based on a strictly screened multicenter cohort of patients with colorectal liver metastases who underwent surgical intervention. First, high-throughput quantitative features are automatically extracted from the 3D regions of interest (ROIs) segmented based on pre-operative magnetic resonance imaging (MRI). Advanced machine learning dimensionality reduction algorithms are subsequently applied to eliminate redundant variables and select core imaging signatures that deeply reflect tumor micro-heterogeneity, microvascular proliferation, and invasive status. On this basis, these micro-level radiomics features are fused with macro-level clinical parameters, including liver function indexes and tumor load markers. Multivariable survival analysis models are performed to identify independent prognostic factors, which are further used to develop a visual and intuitive predictive nomogram tool. Finally, time-dependent evaluation metrics and an external validation cohort will be utilized to systematically test the dynamic predictive performance and cross-platform generalizability of the model. This multimodal dual-track data paradigm aims to achieve a non-invasive and efficient \"digital optical biopsy\" approach for prognostic evaluation.","The study population consists of adult patients diagnosed with colorectal liver metastases who underwent surgical resection across multiple participating clinical centers, featuring high-quality preoperative liver MRI scans and complete clinical follow-up histories.",[20],{"label":21,"type":13,"description":22,"interventionNames":23},"CRLM Surgical Cohort","A multicenter cohort consisting of patients with colorectal liver metastases who underwent surgical resection. Preoperative magnetic resonance imaging (MRI) is utilized for 3D modeling and three-dimensional reconstruction of the liver and lesions, from which key spatial\u002Fmorphological features and systemic clinical parameters are analyzed to predict post-operative recurrence-free survival.",[24],"Procedure: Multimodal Predictive Evaluation",[26],{"type":27,"name":28,"description":29,"armGroupLabels":30,"otherNames":13},"PROCEDURE","Multimodal Predictive Evaluation","Observational and non-invasive assessment based on preoperative MRI-derived 3D modeling and three-dimensional lesion reconstruction, combined with clinical parameters, to build a survival prediction model. No experimental intervention is assigned.",[21],[32],{"name":33,"affiliation":34,"role":35},"Jizhou Wang, MD, PhD","The First Affiliated Hospital of University of Science and Technology of China","PRINCIPAL_INVESTIGATOR",[37],{"name":38,"role":39,"phone":40,"phoneExt":13,"email":41},"can zhang","CONTACT","+8615755077865","15755077865@163.com",[43],{"facility":34,"status":44,"city":45,"state":46,"zip":47,"country":48,"countryCode":49,"cosmosGeoPoint":50,"geoPoint":55,"contacts":56},"RECRUITING","Hefei","Anhui","230001","China","CN",{"type":51,"coordinates":52},"Point",[53,54],117.28083,31.86389,{"lat":54,"lon":53},[57],{"name":38,"role":39,"phone":58,"phoneExt":13,"email":41},"15755077865",{"type":35,"investigatorFullName":60,"investigatorTitle":61,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"Lianxin Liu，PhD","Professor",[63],{"name":64,"class":65},"Anhui Provincial Cancer Hospital","OTHER","100656831","multimodal-survival-prediction-model-for-colorectal-liver-metastases-post-operation-100656831",false,"NCT07830576","Multimodal Survival Prediction Model for Colorectal Liver Metastases Post-Operation","Construction of a Post-operative Multimodal Survival Prediction Model for Patients With Colorectal Liver Metastases: A Study Based on the Fusion of a Multicenter Cohort and High-dimensional Radiomics","Inclusion Criteria:\n\nAge \\>= 18 years old. Confirmed diagnosis of colorectal cancer with liver metastases (CRLM). Underwent surgical resection for colorectal liver metastases. Preoperative liver magnetic resonance imaging (MRI) was performed with high-quality images available for 3D radiomics feature extraction.\n\nComplete baseline clinical, laboratory, and post-operative follow-up survival data.\n\nExclusion Criteria:\n\nPatients who received local ablation only (such as radiofrequency ablation) without surgical resection.\n\nPreoperative MRI images with severe artifacts or poor quality that prevent high-throughput radiomics analysis.\n\nConcurrent history of other primary malignant neoplasms. Missing key clinical variables or lost to follow-up immediately after surgical intervention.","ALL","18 Years","80 Years",{"count":77,"type":78},205,"ESTIMATED","OBSERVATIONAL","The purpose of this study is to construct and validate a multimodal survival prediction model by integrating 3D radiomics features derived from preoperative magnetic resonance imaging (MRI) with systemic clinical baseline indicators for patients with colorectal liver metastases (CRLM) after surgery. By combining micro-level radiomics signatures reflecting tumor micro-heterogeneity with macro-level clinical parameters (such as liver function and tumor biomarkers), the study aims to accurately evaluate individual post-operative prognostic risks. This quantitative tool will provide reliable decision support for clinicians to customize post-operative follow-up and personalized adjuvant treatment strategies.",[82,83,84],"Colorectal Liver Metastases","Colorectal Neoplasms","Liver Neoplasms",[82,86,87,88,89],"Radiomics","Magnetic Resonance Imaging","Nomogram","Prognosis","2026-09-15",{"date":92,"type":93},"2026-09-21","ACTUAL",{"date":95,"type":93},"2017-02-01",{"date":97,"type":78},"2026-09",{"name":5,"class":6},1]