[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"malignant-tumor-with-metastasis\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:malignant-tumor-with-metastasis":25},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,38],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":11,"sex":15,"minAge":16,"maxAge":17,"enrollmentInfo":18,"targetDuration":21,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":26,"whyStopped":4,"lastUpdateSubmitDate":27,"lastUpdatePostDateStruct":28,"startDateStruct":31,"completionDateStruct":33,"leadSponsor":35,"locationsCount":4},"100637514","artificial-intelligence-based-early-warning-for-distant-metastasis-in-malignant-tumors-100637514",false,"NCT07616011","Artificial Intelligence-Based Early Warning for Distant Metastasis in Malignant Tumors","Inclusion Criteria:\n\n1. Aged ≥ 18 years old;\n2. Diagnosed with malignant tumor confirmed by histopathology;\n3. No distant metastasis detected at baseline enrollment assessment;\n4. Regular imaging examinations for distant metastasis assessment are scheduled in the routine follow-up protocol after enrollment;\n5. Complete baseline clinicopathological data are available;\n6. Patients provide informed consent and permit researchers to collect and analyze their subsequent imaging and clinicopathological data.\n\nExclusion Criteria:\n\n1. Concurrent presence of two or more primary malignant tumors;\n2. Presence of any medical or social factors that may interfere with completion of routine imaging follow-up.","ALL","18 Years","95 Years",{"count":19,"type":20},10000,"ESTIMATED","4 Years","OBSERVATIONAL","Early detection and timely intervention of distant metastasis are essential for improving the prognosis of patients with malignant tumors. However, current clinical methods have notable limitations. Conventional imaging can only detect macroscopic metastatic lesions, failing to seize the optimal intervention window before metastasis occurs or during the micrometastasis stage. Previous research has adopted artificial intelligence to break the constraints of traditional imaging and realized subclinical early warning of distant metastasis based on retrospective data. On this basis, the present study aims to systematically validate the predictive performance and generalizability of the model in real-world clinical settings via a prospective cohort. This study intends to establish an organ-specific, non-invasive and cost-effective pan-cancer tool for early warning of distant metastasis. It can gain critical time for clinical intervention, help reduce the incidence of distant metastasis and ultimately optimize patient prognosis.",[25],"Malignant Tumor With Metastasis","NOT_YET_RECRUITING","2026-05-25",{"date":29,"type":30},"2026-05-29","ACTUAL",{"date":32,"type":20},"2026-06-01",{"date":34,"type":20},"2036-12-31",{"name":36,"class":37},"Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University","OTHER",{"id":39,"slug":40,"hasResults":11,"nctId":41,"briefTitle":42,"officialTitle":43,"acronym":4,"eligibilityCriteria":44,"healthyVolunteers":11,"sex":15,"minAge":4,"maxAge":4,"enrollmentInfo":45,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":47,"conditions":48,"keywords":50,"overallStatus":26,"whyStopped":4,"lastUpdateSubmitDate":56,"lastUpdatePostDateStruct":57,"startDateStruct":59,"completionDateStruct":61,"leadSponsor":63,"locationsCount":4},"100617540","evaluating-the-predictive-capability-of-transcriptomic-profiling-for-identifying-the-primary-site-of-metastatic-tumors-100617540","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.",{"count":46,"type":20},30,"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.",[25,49],"CUP",[51,52,53,54,55],"Cancer of unknown primary","Specific RNA transcript","Tissue origin identification","Machine learning","Cancer diagnosis","2025-12-20",{"date":58,"type":30},"2026-01-06",{"date":60,"type":20},"2026-01-15",{"date":62,"type":20},"2026-10-13",{"name":64,"class":37},"Fudan University"]