Ignite Creation Date:
2025-12-25 @ 1:40 AM
Ignite Modification Date:
2026-03-20 @ 11:04 AM
Study NCT ID:
NCT06760494
Status:
COMPLETED
Last Update Posted:
2025-01-06
First Post:
2024-12-28
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Microvascular Invasion Artificial Intelligence Prediction Via Contrast-enhanced Ultrasound With Explainability
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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 250}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-11-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2024-05-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-01-03', 'studyFirstSubmitDate': '2024-12-28', 'studyFirstSubmitQcDate': '2025-01-03', 'lastUpdatePostDateStruct': {'date': '2025-01-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-05-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'area under operating characteristic curves (AUC)', 'timeFrame': 'From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)', 'description': 'the area under operating characteristic curves (AUC) to evaluate the performance of MAPUSE model in predicting MVI in HCC patients'}], 'secondaryOutcomes': [{'measure': 'ACC (accuracy)', 'timeFrame': 'From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)', 'description': 'The ratio of the number of samples correctly predicted by the model to the total number of samples'}, {'measure': 'Specificity', 'timeFrame': 'From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)', 'description': 'Proportion of all patients without MVI who are predicted negative by MAPUSE'}, {'measure': 'Sensitivity', 'timeFrame': 'From preoperative enrollment to the postoperative confirmation of pathological diagnosis (7-15 days postopertively)', 'description': 'The proportion of patients with MVI that MAPUSE correctly identifies'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['hepatocellular carcinoma', 'microvascular invasion', 'contrast-enhanced ultrasound', 'deep learning'], 'conditions': ['HCC - Hepatocellular Carcinoma', 'Microvascular Invasion (MVI)']}, 'descriptionModule': {'briefSummary': 'An artificial intelligence (AI) model to predict MVI of HCC using contrast-enhanced ultrasound was constructed. This model also has biological explainability. The investigators named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability).\n\nThe goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.', 'detailedDescription': 'The presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical prognostic indicator, but its preoperative diagnosis remains challenging. Contrast-enhanced ultrasound (CEUS), with its dynamic microvascular imaging capability, holds promise in prediction of MVI.\n\nThe investigators constructed an artificial intelligence (AI) model to predict MVI using contrast-enhanced ultrasound. This model also has biological explainability. We named it as MAPUSE (MVI AI prediction via contrast-enhanced ultrasound with explainability).\n\nThe goal of MAPUSE study is to prospectively test the performance of MAPUSE model on MVI prediction and its biological correlation in different geographical areas of China.\n\nThe performance of MAPUSE is to be tested in two prospective testing cohorts from two centers in southern and northern China. Before surgery, patient CEUS videos will be collected and analysed by MAPUSE model to generate an MVI risk score. According to the postoperative pathological diagnosis of MVI (golden criterion), the result of MAPUSE will be evaluated. Parameters include area under curve (AUC), accuracy (ACC), sensitivity, specificity and F1-score.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adult patients who underwent surgical treatment for HCC', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age \\>18 years old.\n2. The HCC diagnosis and the presence of MVI were confirmed by surgical pathology.\n3. Complete and clear CEUS videos obtained within two weeks preoperatively.\n\nExclusion Criteria:\n\n1. Unqualified CEUS images.\n2. Missing surgical pathological diagnosis.\n3. Lesions underwent local treatments.\n4. Non-HCC diagnosis'}, 'identificationModule': {'nctId': 'NCT06760494', 'acronym': 'MAPUSE', 'briefTitle': 'Microvascular Invasion Artificial Intelligence Prediction Via Contrast-enhanced Ultrasound With Explainability', 'organization': {'class': 'OTHER', 'fullName': 'Chinese PLA General Hospital'}, 'officialTitle': 'Prediction of Microvascular Invasion in HCC Using Spatiotemporal Radiomics of Contrast-enhanced Ultrasound: a Deep Learning Model With Transcriptomics Correlation', 'orgStudyIdInfo': {'id': 'MAPUSE'}, 'secondaryIdInfos': [{'id': '92159305', 'type': 'OTHER', 'domain': 'The National Natural Science Foundation of China'}, {'id': '82030047', 'type': 'OTHER', 'domain': 'The National Natural Science Foundation of China'}, {'id': '82325027', 'type': 'OTHER', 'domain': 'The National Natural Science Foundation of China'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Chinese PLA General Hospital Cohort', 'description': 'Patients from Chinese PLA General Hospital (northern China) after surgical treatment', 'interventionNames': ['Diagnostic Test: the MAPUSE model']}, {'label': 'the First Affiliated Hospital of Sun Yat-sen University Cohort', 'description': 'Patients from the First Affiliated Hospital of Sun Yat-sen University (southern China) after surgical treatment', 'interventionNames': ['Diagnostic Test: the MAPUSE model']}], 'interventions': [{'name': 'the MAPUSE model', 'type': 'DIAGNOSTIC_TEST', 'description': 'Using the MAPUSE model to predict MVI status before surgical resection for HCC patients', 'armGroupLabels': ['Chinese PLA General Hospital Cohort', 'the First Affiliated Hospital of Sun Yat-sen University Cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510080', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'the First Affiliated Hospital of Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'zip': '100853', 'city': 'Beijing', 'country': 'China', 'facility': 'Chinese PLA General Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'overallOfficials': [{'name': 'Chuan Pang', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Chinese PLA General Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese PLA General Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'Chinese Academy of Sciences', 'class': 'OTHER_GOV'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Ping Liang', 'investigatorAffiliation': 'Chinese PLA General Hospital'}}}}