[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100617540":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":17,"studyPopulation":18,"armGroups":13,"interventions":13,"overallOfficials":13,"centralContacts":19,"locations":13,"responsibleParty":25,"collaborators":13,"id":29,"slug":13,"hasResults":30,"nctId":31,"briefTitle":32,"officialTitle":33,"acronym":13,"eligibilityCriteria":34,"healthyVolunteers":30,"sex":35,"minAge":13,"maxAge":13,"enrollmentInfo":36,"targetDuration":13,"studyType":39,"phases":13,"briefSummary":40,"conditions":41,"keywords":44,"overallStatus":50,"whyStopped":13,"lastUpdateSubmitDate":51,"lastUpdatePostDateStruct":52,"startDateStruct":55,"completionDateStruct":57,"leadSponsor":59,"locationsCount":13},{"fullName":5,"class":6},"Fudan University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"The accuracy of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors","Overall accuracy of the tumor tissue origin tracing transcriptome test for predicting the primary site of metastatic tumors (number of correct predictions\u002Ftotal evaluable cases); Specificity \\[true negatives\u002F(true negatives + false positives)\\]; Sensitivity \\[true positives\u002F(true positives + false negatives)\\].","through study completion, an average of 1 year",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"CASE_ONLY","PROSPECTIVE","This is a non-interventional, observational study. Through a single-center, prospective clinical trial, the study aims to utilize the transcriptomic profiling for tumor tissue origin identification to predict the tissue origin of primary sites in metastatic tumors and evaluate the accuracy and specificity of this prediction solution.\n\nPrimary Endpoint:\n\nThe accuracy of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors (expressed as overall accuracy with its 95% confidence interval).\n\nSecondary Endpoints:\n\n1. The specificity and sensitivity of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors.\n2. Exploratory analysis of characteristic molecular markers expressed in metastatic lesions from different primary sites.","Patients treated in Fudan University Shanghai Cancer Center, encompassing both outpatient and inpatient populations.",[20],{"name":21,"role":22,"phone":23,"phoneExt":13,"email":24},"Zhiao Chen, Ph.D.","CONTACT","008618017312074","zachen@fudan.edu.cn",{"type":26,"investigatorFullName":27,"investigatorTitle":28,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"PRINCIPAL_INVESTIGATOR","Zhimin Shao","Director of the Department of Breast Surgery, Fudan University Shanghai Cancer Center","100617540",false,"NCT07319949","Evaluating the Predictive Capability of Transcriptomic Profiling for Identifying the Primary Site of Metastatic Tumors","Evaluative Study on Predicting the Primary Site of Metastatic Tumors Using Transcriptomic Profiling for Tumor Tissue Origin Identification","Inclusion Criteria\n\n1. Clinically confirmed diagnosis of malignant tumor with metastasis;\n2. Metastatic lesions confirmed as malignant by histopathology;\n3. Sufficient surgical resection or biopsy specimens retained to meet the requirements for next-generation sequencing;\n4. The participant (or their legal representative\u002Fguardian) has signed the informed consent form, confirming full understanding of the study's purpose and procedures, and voluntarily agrees to participate.\n\nExclusion Criteria:\n\n1\\. The investigator deems the patient unable to provide informed consent.","ALL",{"count":37,"type":38},30,"ESTIMATED","OBSERVATIONAL","This study will enroll patients with metastatic malignancies. Tumor samples (fresh or formalin-fixed paraffin-embedded tissue specimens) will undergo RNA extraction and next-generation sequencing (RNA-seq). Once the raw data is obtained, the system will analyze the transcriptomic feature values (cancer-specific RNA transcripts and tissue-specific RNA transcripts) expressed in the tumor tissue samples to further predict tissue origin using a machine learning model. The output includes probabilities and confidence intervals for tissue origin.",[42,43],"Malignant Tumor With Metastasis","CUP",[45,46,47,48,49],"Cancer of unknown primary","Specific RNA transcript","Tissue origin identification","Machine learning","Cancer diagnosis","NOT_YET_RECRUITING","2025-12-20",{"date":53,"type":54},"2026-01-06","ACTUAL",{"date":56,"type":38},"2026-01-15",{"date":58,"type":38},"2026-10-13",{"name":5,"class":6}]