Ignite Creation Date:
2025-12-25 @ 4:50 AM
Ignite Modification Date:
2026-03-11 @ 6:17 AM
Study NCT ID:
NCT04217018
Status:
RECRUITING
Last Update Posted:
2021-02-08
First Post:
2019-12-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D005910', 'term': 'Glioma'}], 'ancestors': [{'id': 'D018302', 'term': 'Neoplasms, Neuroepithelial'}, {'id': 'D017599', 'term': 'Neuroectodermal Tumors'}, {'id': 'D009373', 'term': 'Neoplasms, Germ Cell and Embryonal'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D009380', 'term': 'Neoplasms, Nerve Tissue'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'All participants have signed the informed consent. Fresh frozen tissues of participants are collected immediately after tumor resection and preserved in liquid nitrogen. Whole exome sequencing, RNA sequencing and proteomics are planed to be conducted.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3000}, 'targetDuration': '120 Months', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2017-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-02', 'completionDateStruct': {'date': '2027-06-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-02-05', 'studyFirstSubmitDate': '2019-12-30', 'studyFirstSubmitQcDate': '2019-12-31', 'lastUpdatePostDateStruct': {'date': '2021-02-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-01-03', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-01-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AUC of prediction performance', 'timeFrame': 'up to 10 years', 'description': 'AUC=sensitivity+specificity-1'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['molecular', 'radiomics', 'deep learning', 'machine learning'], 'conditions': ['Glioma']}, 'referencesModule': {'references': [{'pmid': '37697238', 'type': 'DERIVED', 'citation': 'Liu Z, Hong X, Wang L, Ma Z, Guan F, Wang W, Qiu Y, Zhang X, Duan W, Wang M, Sun C, Zhao Y, Duan J, Sun Q, Liu L, Ding L, Ji Y, Yan D, Liu X, Cheng J, Zhang Z, Li ZC, Yan J. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer. 2023 Sep 11;23(1):848. doi: 10.1186/s12885-023-11338-8.'}]}, 'descriptionModule': {'briefSummary': 'This registry aims to collect clinical, molecular and radiologic data including detailed clinical parameters, molecular pathology (1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations, etc) and conventional/advanced/new MR sequences (T1, T1c, T2, FLAIR, ADC, DTI, PWI, etc) of patients with primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine algorithms that able to predict molecular pathology or subgroups of gliomas.', 'detailedDescription': 'Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, radiological data and with sufficient sample size for deep learning (\\>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'maximumAge': '95 Years', 'minimumAge': '1 Year', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with newly diagnosed glioma that receive tumor resection', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients must have radiologically and histologically confirmed diagnosis of primary glioma\n* Life expectancy of greater than 3 months\n* Must receive tumor resection\n* Signed informed consent\n\nExclusion Criteria:\n\n* No gliomas\n* No sufficient amount of tumor tissues for detection of molecular pathology\n* Patients who have any type of bioimplant activated by mechanical, electronic, or magnetic devices\n* Patients who are pregnant or breast feeding\n* Patients who are suffered from severe systematic malfunctions'}, 'identificationModule': {'nctId': 'NCT04217018', 'briefTitle': 'MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence', 'organization': {'class': 'OTHER', 'fullName': 'The First Affiliated Hospital of Zhengzhou University'}, 'officialTitle': 'MR Based Prediction of Molecular Biomarkers or Subgroups in Primary Glioma Using Deep Learning or Machine Learning', 'orgStudyIdInfo': {'id': 'GliomaAI-1'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Prediction of molecular pathology', 'type': 'DIAGNOSTIC_TEST', 'description': 'Prediction of 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations or molecular subgroups by leveraging AI'}]}, 'contactsLocationsModule': {'locations': [{'zip': '450052', 'city': 'Zhengzhou', 'state': 'Henan', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhenyu Zhang, Dr.', 'role': 'CONTACT', 'email': 'fcczhangzy1@zzu.edu.cn', 'phone': '+86 17839973727'}], 'facility': 'Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University', 'geoPoint': {'lat': 34.75778, 'lon': 113.64861}}], 'centralContacts': [{'name': 'Zhenyu Zhang, Dr.', 'role': 'CONTACT', 'email': 'fcczhangzy1@zzu.edu.cn', 'phone': '+86 17839973727'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED', 'description': 'Undecided.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The First Affiliated Hospital of Zhengzhou University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Sun Yat-sen University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Zhenyu Zhang', 'investigatorAffiliation': 'The First Affiliated Hospital of Zhengzhou University'}}}}