Ignite Creation Date:
2025-12-24 @ 2:17 PM
Ignite Modification Date:
2026-02-23 @ 11:35 AM
Study NCT ID:
NCT02329795
Status:
TERMINATED
Last Update Posted:
2017-10-12
First Post:
2014-12-16
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Image-derived Prediction of Response to Chemo-radiation in Glioblastoma
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D005909', 'term': 'Glioblastoma'}], 'ancestors': [{'id': 'D001254', 'term': 'Astrocytoma'}, {'id': 'D005910', 'term': 'Glioma'}, {'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'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D011878', 'term': 'Radiotherapy'}, {'id': 'D000077204', 'term': 'Temozolomide'}], 'ancestors': [{'id': 'D013812', 'term': 'Therapeutics'}, {'id': 'D003606', 'term': 'Dacarbazine'}, {'id': 'D014226', 'term': 'Triazenes'}, {'id': 'D009930', 'term': 'Organic Chemicals'}, {'id': 'D007093', 'term': 'Imidazoles'}, {'id': 'D001393', 'term': 'Azoles'}, {'id': 'D006573', 'term': 'Heterocyclic Compounds, 1-Ring'}, {'id': 'D006571', 'term': 'Heterocyclic Compounds'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 16}, 'patientRegistry': False}, 'statusModule': {'whyStopped': 'Slow accrual', 'overallStatus': 'TERMINATED', 'startDateStruct': {'date': '2014-10'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2017-10', 'completionDateStruct': {'date': '2016-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2017-10-11', 'studyFirstSubmitDate': '2014-12-16', 'studyFirstSubmitQcDate': '2014-12-29', 'lastUpdatePostDateStruct': {'date': '2017-10-12', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2015-01-01', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2016-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Sensitivity and specificity of predicted response', 'timeFrame': '3 months post radiotherapy', 'description': 'Tumor response is measured as contrast-enhancing tumor on T1-weighted MRI and by metabolic active tumor using 18F-fluroethyl-tyrosine (FET)-PET. Pre-treatment risk map is constructed using machine learning methods and compared to post-treatment scans.'}], 'secondaryOutcomes': [{'measure': 'DICE-similarity coefficient and percentage overlap of 64Cu-ATSM and contrast-enhanced T1-weighted MRI', 'timeFrame': '3 months post radiotherapy', 'description': 'Pre-chemoradiotherapy 64Cu-ATSM-PET is used as a surrogate marker for hypoxia and compared to treatment response, measured as contrast-enhancing tumor on T1-weighted MRI'}, {'measure': 'Correlation (volume and maximum values) between lactate and hypoxia', 'timeFrame': '1 week before start of chemoradiotherapy', 'description': 'Lactate measured by MR spectroscopy is compared to metabolic uptake of 64Cu-ATSM-PET'}]}, 'oversightModule': {'oversightHasDmc': True}, 'conditionsModule': {'conditions': ['Glioblastoma']}, 'descriptionModule': {'briefSummary': 'This study seeks to investigate if advanced image-analysis of diagnostic scans, can be used to predict how aggressive brain tumors (glioblastoma) respond to standard chemo- and radiation treatment.', 'detailedDescription': 'Generally, response prediction models seeks to predict time to an event, e.g. time-to-progression and/or overall survival. The aim of this study is to explore the feasibility of establishing an individualized response model, that, based on several morphologic, physiologic and metabolic parameters extracted from computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI), is able to predict the tumor response at the level of an imaging voxel, using machine learning techniques.\n\nImaging modalities include MRI, PET/CT with 18F-fluroethyltyrosine (18F-FET), and PET/MRI with 64Cu-diacetyl-bis(N4-methylthiosemicarbazone) (64Cu-ATSM).'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with primary glioblastoma, eligible for chemoradiotherapy.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Histologically confirmed, primary supratentorial glioblastoma (WHO grade IV).\n\nExclusion Criteria:\n\n* No informed consent can be obtained\n* Inability to undergo MRI examination, due to metal implants, pacemaker etc.\n* Not eligible for Stupp-regimen'}, 'identificationModule': {'nctId': 'NCT02329795', 'acronym': 'IDEPREG', 'briefTitle': 'Image-derived Prediction of Response to Chemo-radiation in Glioblastoma', 'organization': {'class': 'OTHER', 'fullName': 'Rigshospitalet, Denmark'}, 'officialTitle': 'Image-derived Prediction of Response to Chemo-radiation in Patients With Glioblastoma', 'orgStudyIdInfo': {'id': '192/13'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Standard chemoradiotherapy', 'description': 'Group to receive 60 Gy radiotherapy in 30 fractions with concomitant and adjuvant temozolomide.', 'interventionNames': ['Radiation: Radiotherapy', 'Drug: Temozolomide']}], 'interventions': [{'name': 'Radiotherapy', 'type': 'RADIATION', 'description': '60 Gy in 30 fractions, 5 days a week, modulated arc therapy.', 'armGroupLabels': ['Standard chemoradiotherapy']}, {'name': 'Temozolomide', 'type': 'DRUG', 'otherNames': ['Temodar'], 'description': 'Concomitant: 75 mg/m2 5 days a week from start of radiotherapy. Adjuvant: 150/200 mg/m2 in 5 days per 28 days in 6 months.', 'armGroupLabels': ['Standard chemoradiotherapy']}]}, 'contactsLocationsModule': {'locations': [{'zip': '2100', 'city': 'Copenhagen', 'country': 'Denmark', 'facility': 'Department of Oncology, Section for Radiotherapy, Rigshospitalet', 'geoPoint': {'lat': 55.67594, 'lon': 12.56553}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Rigshospitalet, Denmark', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Ph.d.-fellow, M.Sc.', 'investigatorFullName': 'Michael Lundemann Jensen', 'investigatorAffiliation': 'Rigshospitalet, Denmark'}}}}