Ignite Creation Date:
2026-03-26 @ 3:20 PM
Ignite Modification Date:
2026-03-31 @ 5:17 AM
Study NCT ID:
NCT07444905
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2026-03-03
First Post:
2026-02-24
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Evaluation of a Machine Learning-Based Prediction Strategy for Extrahepatic Metastasis in Hepatocellular Carcinoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006528', 'term': 'Carcinoma, Hepatocellular'}], 'ancestors': [{'id': 'D000230', 'term': 'Adenocarcinoma'}, {'id': 'D002277', 'term': 'Carcinoma'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008113', 'term': 'Liver Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D008107', 'term': 'Liver Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 800}, 'targetDuration': '36 Months', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2023-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-02', 'studyFirstSubmitDate': '2026-02-24', 'studyFirstSubmitQcDate': '2026-03-02', 'lastUpdatePostDateStruct': {'date': '2026-03-03', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-03', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Incident Extrahepatic Metastasis (EHM)', 'timeFrame': 'Up to 36 months', 'description': 'New extrahepatic metastatic disease detected after baseline and confirmed by imaging (CT/MRI/PET-CT) and/or histopathology, documented in the medical record.'}], 'secondaryOutcomes': [{'measure': 'Overall survival (OS)', 'timeFrame': 'Up to 36 months', 'description': 'Time from baseline (enrollment) to death from any cause; censored at last known alive date.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['HCC - Hepatocellular Carcinoma', 'Machine Learning', 'Evaluation']}, 'descriptionModule': {'briefSummary': 'This is a multicenter prospective observational cohort study in patients with hepatocellular carcinoma (HCC) after curative-intent treatment. The study aims to prospectively validate previously developed machine learning-based risk stratification models for extrahepatic metastasis (with a focus on lung and bone metastasis) and to evaluate their potential clinical utility in real-world postoperative surveillance and management pathways.\n\nThe study does not assign treatments or surveillance strategies to participants. Clinical care is determined by treating physicians according to local practice. The study will assess model performance (including discrimination and calibration), risk stratification ability, and implementation-related outcomes such as model adoption, decision impact, and changes in monitoring intensity or referral pathways. The study will also explore clinical and resource-related outcomes associated with model-informed risk stratification in routine practice.', 'detailedDescription': 'Background and Rationale\n\nHepatocellular carcinoma (HCC) is associated with a substantial risk of postoperative recurrence and extrahepatic metastasis, which can significantly affect prognosis and treatment opportunities. Early identification of patients at high risk of extrahepatic metastasis may support more appropriate surveillance intensity, earlier multidisciplinary evaluation, and more timely treatment planning. However, risk-stratified postoperative surveillance strategies for extrahepatic metastasis are not well established in routine clinical practice.\n\nThe investigators previously developed machine learning-based risk stratification models for extrahepatic metastasis in retrospective multicenter cohorts, with particular focus on lung and bone metastasis. The present study is designed to prospectively validate these models and evaluate their potential value in real-world clinical workflow and decision-making.\n\nStudy Objectives Primary Objective\n\nTo prospectively validate the performance of pre-specified machine learning-based risk stratification models for postoperative extrahepatic metastasis risk in HCC (especially lung and bone metastasis), including discrimination and calibration in a multicenter real-world setting.\n\nSecondary Objectives\n\nTo evaluate risk stratification performance across clinically relevant subgroups and participating centers.To assess implementation-related outcomes in routine practice, including model adoption and clinical decision impact.To evaluate whether model-informed risk stratification is associated with changes in postoperative surveillance patterns, referral pathways, and timing of multidisciplinary assessment.To explore clinical outcome signals (for example, time-to-event outcomes and clinically actionable detection window-related outcomes) associated with model-informed risk stratification.To explore resource utilization and health economic implications of model-informed postoperative management in real-world settings.\n\nStudy Design\n\nThis is a multicenter, prospective, observational cohort study. Participants are enrolled and followed according to routine clinical care at participating hospitals. This study does not involve randomization, mandated intervention assignment, or protocol-driven treatment allocation. The study is intended to validate and evaluate a risk stratification tool and its real-world implementation, rather than to test an interventional treatment.\n\nThe risk models, variables, and prespecified risk stratification framework were developed prior to initiation of prospective enrollment. Prospective data collection is used to evaluate model transportability, calibration, clinical utility, and implementation characteristics in independent real-world cohorts.\n\nStudy Population\n\nEligible participants are adults with hepatocellular carcinoma who have undergone curative-intent treatment and are entering postoperative follow-up. Detailed inclusion and exclusion criteria are provided in the protocol and include availability of required baseline clinical variables and follow-up information for outcome assessment.\n\nStudy Procedures and Data Collection\n\nData are collected prospectively during routine care and may include:\n\nBaseline demographic and clinical characteristics；Tumor-related and treatment-related variables；Follow-up imaging and laboratory surveillance information；Occurrence and timing of extrahepatic metastasis (especially lung and bone metastasis)\n\nRecurrence and survival-related outcomes, where available\n\nClinical management decisions (for example, surveillance intensity, referrals, multidisciplinary team discussion, and treatment planning)\n\nImplementation metrics (for example, model use/adoption and decision impact documentation, where available)\n\nResource utilization variables for exploratory economic analyses, where available\n\nNo study-mandated treatment is assigned. Clinical decisions remain under the responsibility of treating physicians.\n\nOutcomes and Analytic Framework\n\nThe study will evaluate model performance using prespecified statistical methods, which may include measures of discrimination, calibration, and clinical utility for time-to-event outcomes. Analyses may also assess center-level heterogeneity, temporal performance, and model updating/recalibration strategies where appropriate.\n\nImplementation and clinical utility analyses may include evaluation of:\n\nAdoption and use of the risk stratification tool\n\nChanges in surveillance intensity or follow-up pathways associated with risk strata\n\nDecision impact on referral and management planning\n\nExploratory associations with clinically meaningful outcomes and resource use\n\nAny analyses intended to emulate hypothetical management strategies (for example, target trial emulation-based analyses) will be clearly reported as observational and estimated/hypothetical, and will not be interpreted as randomized treatment effects.\n\nStudy Significance\n\nThis study is expected to provide prospective multicenter evidence on the validity and real-world clinical utility of machine learning-based risk stratification for extrahepatic metastasis in HCC. The results may inform postoperative surveillance optimization, risk-adapted management pathways, and future implementation research.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adult patients with hepatocellular carcinoma (HCC) without evidence of extrahepatic metastasis at baseline who are receiving standard-of-care management and undergoing prospective follow-up in routine clinical practice. Participants are enrolled in a prospective observational cohort to evaluate a machine learning-based risk assessment strategy for predicting extrahepatic metastasis and supporting risk-adapted surveillance.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age ≥ 18 years.\n* Diagnosis of hepatocellular carcinoma (HCC) confirmed by histopathology or accepted radiologic criteria per international guidelines.\n* No evidence of extrahepatic metastasis at baseline evaluation.\n* Receiving standard-of-care management with planned longitudinal follow-up in routine clinical practice.\n* Availability of baseline clinical and imaging data required for risk assessment using the pre-specified machine learning model.\n* Ability to provide written informed consent, or inclusion under ethics committee-approved procedures.\n\nExclusion Criteria:\n\n* Confirmed extrahepatic metastasis at enrollment (baseline).\n* History of other active malignancy within the past 5 years, except adequately treated non-melanoma skin cancer or in situ carcinoma.\n* Incomplete baseline clinical information that precludes model-based risk assessment.\n* Expected survival \\< 3 months due to severe comorbidities.\n* Participation in an interventional clinical trial that may substantially alter follow-up strategy or metastasis assessment.'}, 'identificationModule': {'nctId': 'NCT07444905', 'briefTitle': 'Evaluation of a Machine Learning-Based Prediction Strategy for Extrahepatic Metastasis in Hepatocellular Carcinoma', 'organization': {'class': 'OTHER', 'fullName': 'Tongji Hospital'}, 'officialTitle': 'Evaluating a Machine Learning-Based Strategy for Predicting Extrahepatic Metastasis and Guiding Risk-Adapted Surveillance in Hepatocellular Carcinoma', 'orgStudyIdInfo': {'id': 'MLEHM-010'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Prospective Cohort', 'description': 'Participants with hepatocellular carcinoma enrolled in a prospective observational cohort to evaluate a machine learning-based strategy for predicting extrahepatic metastasis and supporting risk-adapted surveillance. No experimental interventions are assigned, and patients receive standard clinical management.', 'interventionNames': ['Other: Machine Learning-Based Risk Assessment']}], 'interventions': [{'name': 'Machine Learning-Based Risk Assessment', 'type': 'OTHER', 'description': 'Risk stratification using a previously developed machine learning model; no treatment assignment is performed.', 'armGroupLabels': ['Prospective Cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '430030', 'city': 'Wuhan', 'state': 'Hubei', 'country': 'China', 'facility': 'Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}], 'overallOfficials': [{'name': 'Zhao Huang', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Tongji Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tongji Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'professor', 'investigatorFullName': 'Chen Xiaoping', 'investigatorAffiliation': 'Tongji Hospital'}}}}