Ignite Creation Date:
2025-12-25 @ 4:54 AM
Ignite Modification Date:
2025-12-26 @ 3:54 AM
Study NCT ID:
NCT06735118
Status:
RECRUITING
Last Update Posted:
2024-12-16
First Post:
2024-11-27
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D065626', 'term': 'Non-alcoholic Fatty Liver Disease'}], 'ancestors': [{'id': 'D005234', 'term': 'Fatty Liver'}, {'id': 'D008107', 'term': 'Liver Diseases'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D020360', 'term': 'Neoadjuvant Therapy'}], 'ancestors': [{'id': 'D003131', 'term': 'Combined Modality Therapy'}, {'id': 'D013812', 'term': 'Therapeutics'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 120}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-12-25', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2024-12-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-12-13', 'studyFirstSubmitDate': '2024-11-27', 'studyFirstSubmitQcDate': '2024-12-13', 'lastUpdatePostDateStruct': {'date': '2024-12-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Extract the whole liver fat fraction', 'timeFrame': 'one year', 'description': 'Extract the whole liver fat fraction using the threshold extraction method.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Non-Alcoholic Fatty Liver Disease']}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to quantitatively assess the changes in liver fat content in cancer patients before and after treatment.\n\nThe main questions it aims to answer are:How does the liver fat fraction change before and after chemotherapy? In this study, patients undergoing mDixon Quant scanning are subjected to fully automated segmentation and measurement of liver fat content using artificial intelligence.', 'detailedDescription': 'Regarding the extraction of liver fat fraction, the traditional axial ROI method involves selecting several regions of interest (ROIs) at the largest cross-sectional level or across multiple continuous sections, and taking the average value as the whole-liver fat fraction. This method is complex, time-consuming, and cannot obtain the whole-liver fat fraction. In this study, a threshold extraction method is used to obtain the whole-liver fat fraction, enabling a 2D-to-3D conversion, which is more time-efficient and labor-saving, and provides a more accurate measurement.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. CT/B ultrasound showed no fatty liver\n2. No MRI contraindications, including pacemaker, stent, metal implant, or claustrophobia\n3. Received neoadjuvant/adjuvant chemotherapy\n\nExclusion Criteria:\n\n1. Missing follow-up information\n2. Liver lesions (metastases, hemangioma, etc.)\n3. Poor image quality'}, 'identificationModule': {'nctId': 'NCT06735118', 'briefTitle': 'Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver', 'organization': {'class': 'OTHER', 'fullName': 'Yunnan Cancer Hospital'}, 'officialTitle': 'Feasibility Study of Deep Learning-based MDixon Quant for Quantitative Assessment of Chemotherapy-induced Fatty Liver', 'orgStudyIdInfo': {'id': 'KYLX2023-165'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'neoadjuvant chemotherapy group', 'description': 'Cancer patients undergoing chemotherapy.', 'interventionNames': ['Drug: Neoadjuvant chemotherapy']}, {'type': 'NO_INTERVENTION', 'label': 'Non-neoadjuvant chemotherapy group', 'description': 'Cancer patients not undergoing chemotherapy.'}], 'interventions': [{'name': 'Neoadjuvant chemotherapy', 'type': 'DRUG', 'otherNames': ['Non'], 'description': 'Neoadjuvant chemotherapy', 'armGroupLabels': ['neoadjuvant chemotherapy group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '650118', 'city': 'Kunming', 'state': 'Yunnan', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Guojun Zhang, Professor', 'role': 'CONTACT', 'phone': '0871-68173640'}], 'facility': 'Yunnan Cancer Hospital', 'geoPoint': {'lat': 25.03889, 'lon': 102.71833}}], 'centralContacts': [{'name': 'Lizhu Liu, Graduate', 'role': 'CONTACT', 'email': 'liulizhu2022@163.com', 'phone': '18287509587'}, {'name': 'Zhenhui Li, MD', 'role': 'CONTACT', 'email': 'lizhenhui@kmmu.edu.cn', 'phone': '13698736132'}], 'overallOfficials': [{'name': 'Lianhua Ye', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Ethics Committee of Yunnan Provincial Cancer Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Yunnan Cancer Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}