Ignite Creation Date:
2025-12-25 @ 1:41 AM
Ignite Modification Date:
2025-12-31 @ 10:47 AM
Study NCT ID:
NCT06798194
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-29
First Post:
2025-01-23
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Fusing Ultrasound and Magnetic Resonance Imaging to Intelligently Plan Highly Conformal Ablation Thermal Field for Hepatocellular Carcinoma
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': 'ESTIMATED', 'count': 300}, 'targetDuration': '2 Years', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-01-30', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2027-12-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-23', 'studyFirstSubmitDate': '2025-01-23', 'studyFirstSubmitQcDate': '2025-01-23', 'lastUpdatePostDateStruct': {'date': '2025-01-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-29', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Local tumor progression', 'timeFrame': '2 years', 'description': 'After HCC ablation, tumor recurrence appears around the ablation area'}], 'secondaryOutcomes': [{'measure': 'tumor recurrence', 'timeFrame': '2 year', 'description': 'Tumor recurrence after HCC ablation'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Hepatocellular Carcinoma']}, 'descriptionModule': {'briefSummary': 'Thermal ablation is an important minimally invasive treatment for hepatocellular carcinoma (HCC), but local tumor progression (LTP) after ablation restricts the efficacy and status of ablation technology and seriously threatens patient survival. Insufficient coverage of thermal field is an important factor on the occurrence of LTP. Current thermal field planning relies on tumor contours and doctor experience, and the safety margin is uniform. Therefore, it cannot cope with the problem of insufficient coverage of thermal field caused by the different invasion capabilities of different tumors and different parts of the same tumor. This project intends to integratively analyze gray-scale ultrasound, contrast-enhanced ultrasound, magnetic resonance imaging and clinical information of HCC through deep canonical correlation analysis; summarize the prior knowledge of LTP risk factors in previous studies and perform conjoint analysis individual case data and common conclusions through knowledge graph; interpretatively predict the LTP risk and the high-risk LTP locations through link prediction; accurately predict the ablation safety margin required for different tumor parts through graph neural network, and achieve highly conformal thermal field planning based on different invasion capabilities to minimize the LTP risk of HCC. The project leverages tumor multi-modal imaging and prior knowledge as the entry point, performs highly conformal planning of the ablation thermal field through artificial intelligence technology, and provides a new method for precise ablation.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'HCC patients receiving ablation therapy', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Pathologically confirmed primary hepatocellular carcinoma\n2. Undergo curative ablation\n3. With complete clinical information and pre- and post-operative imaging information\n\nExclusion Criteria:\n\n1. Undergo palliative ablation\n2. Lack of clinical or imaging information\n3. Age less than 18 years'}, 'identificationModule': {'nctId': 'NCT06798194', 'briefTitle': 'Fusing Ultrasound and Magnetic Resonance Imaging to Intelligently Plan Highly Conformal Ablation Thermal Field for Hepatocellular Carcinoma', 'organization': {'class': 'OTHER', 'fullName': 'Chinese PLA General Hospital'}, 'officialTitle': 'Fusing Ultrasound and Magnetic Resonance Imaging to Intelligently Plan Highly Conformal Ablation Thermal Field for Hepatocellular Carcinoma', 'orgStudyIdInfo': {'id': 'Highly conformal ablation'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Post-ablation MRI is used to evaluate whether the ablation area of the tumor is consistent with the highly conformal ablation thermal field provided by the AI model.', 'type': 'DIAGNOSTIC_TEST', 'description': 'This study developed an AI model that can provide optimal highly conformal ablation thermal field for HCC patients using ultrasound and MRI. Post-ablation MRI is used to evaluate whether the ablation area of the tumor is consistent with the highly conformal ablation thermal field provided by the AI model.\n\nThe patients were divided into:\n\n1. Actual ablation zone of the tumor was consistent with the highly conformal ablation thermal field (consistent group);\n2. Actual ablation zone of the tumor was inconsistent with the highly conformal ablation thermal field (inconsistent group).'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Beijing', 'country': 'China', 'contacts': [{'name': 'Wenzhen Ding', 'role': 'CONTACT', 'email': '923345765@qq.com', 'phone': '+86 66939530'}], 'facility': 'Chinese PLA Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'centralContacts': [{'name': 'Wenzhen Ding, Dr', 'role': 'CONTACT', 'email': '923345765@qq.com', 'phone': '+86 66939530'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese PLA General Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'M.D.', 'investigatorFullName': 'Ping Liang', 'investigatorAffiliation': 'Chinese PLA General Hospital'}}}}