Ignite Creation Date:
2025-12-24 @ 2:35 PM
Ignite Modification Date:
2025-12-26 @ 9:49 AM
Study NCT ID:
NCT06637059
Status:
COMPLETED
Last Update Posted:
2024-10-15
First Post:
2024-10-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificially Intelligent Model for Accurate Detection of HCC
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, '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'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D019370', 'term': 'Observation'}], 'ancestors': [{'id': 'D008722', 'term': 'Methods'}, {'id': 'D008919', 'term': 'Investigative Techniques'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1092}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-10', 'completionDateStruct': {'date': '2024-10-01', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-10-10', 'studyFirstSubmitDate': '2024-10-05', 'studyFirstSubmitQcDate': '2024-10-10', 'lastUpdatePostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnosis of liver disease through CT imaging', 'timeFrame': '1 month'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Hepatocellular carcinoma; Early detection; Multi-modal; Fusion model; Artificial intelligence'], 'conditions': ['Hepatocellular Carcinoma (HCC)']}, 'descriptionModule': {'briefSummary': 'Purpose: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. The investigators aimed to develop a model with multi-modal features (MMF) using artificial intelligence (AI) approaches to enhance the performance of HCC detection.\n\nExperimental Design: A total of 1,092 participants were enrolled from 16 centers. These participants were allocated into the training, internal validation, and external validation cohorts. Peripheral blood specimens were collected prospectively and subjected to mass cytometry analysis. Clinical and radiological data were obtained from electrical medical records. Various AI methods were employed to identify pertinent features and construct single-modal models with optimal performance. The XGBoost algorithm was utilized to amalgamate these models, integrating multi-modal information and facilitating the development of a fusion model. Model evaluation and interpretability were demonstrated using the SHapley Additive exPlanations method.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Benign liver diseases, including but not limited to, hemangiomas, hepatic cysts, focal nodular hyperplasia, and cirrhosis were considered in this study. Participants who had undergone previous treatment for HCC or benign liver diseases, those who had taken medications affecting the hematological system within 2 weeks, or those who had received a blood transfusion within 6 months were excluded from the study.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Benign liver diseases, including but not limited to, hemangiomas, hepatic cysts, focal nodular hyperplasia, and cirrhosis\n\nExclusion Criteria:\n\n* Participants who had undergone previous treatment for HCC or benign liver diseases,\n* had taken medications affecting the hematological system within 2 weeks\n* those who had received a blood transfusion within 6 months'}, 'identificationModule': {'nctId': 'NCT06637059', 'briefTitle': 'Artificially Intelligent Model for Accurate Detection of HCC', 'organization': {'class': 'OTHER', 'fullName': 'Zhejiang University'}, 'officialTitle': 'Construction of an Artificially Intelligent Model for Accurate Detection of HCC by Integrating Clinical, Radiological, and Peripheral Immunological Features', 'orgStudyIdInfo': {'id': 'MMFAIHCC'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Training cohort', 'interventionNames': ['Other: observational study']}, {'label': 'Internal validation cohort', 'interventionNames': ['Other: observational study']}, {'label': 'External validation cohort', 'interventionNames': ['Other: observational study']}], 'interventions': [{'name': 'observational study', 'type': 'OTHER', 'description': 'observation alone', 'armGroupLabels': ['External validation cohort', 'Internal validation cohort', 'Training cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '310003', 'city': 'Hangzhou', 'state': 'Zhejiang', 'country': 'China', 'geoPoint': {'lat': 30.29365, 'lon': 120.16142}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Zhejiang University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'TingBo Liang', 'investigatorAffiliation': 'Zhejiang University'}}}}