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| Name | Class |
|---|---|
| University of Liege | OTHER |
| Hospital de Manises | OTHER |
| Hospital de la Ribera | OTHER |
| Hospital Vall d'Hebron |
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Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The relationship between non-invasive Magnetic Resonance Imaging (MRI) biomarkers at preoperative, postradiotherapy and follow-up stages, and the survival time in GBM patients will be useful to plan an optimal strategy for the management of the disease.
The Hemodynamic Multiparametric Tissue Signature (HTS) biomarker provides an automated unsupervised method to describe the heterogeneity of the enhancing tumor and edema areas in terms of the angiogenic process located at these regions. This allows to automatically draw 4 reproducible habitats that describe the tumor vascular heterogeneity:
The conceptual hypothesis is that there is a significant correlation between the perfusion biomarkers located at several HTS habitats and the patient's overall survival.
The primary purpose of this clinical study is to determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using the HTS biomarker.
This is a multicenter observational retrospective study with data collected from Hospital Information System (HIS) and Picture Archiving and Communication System (PACS) of each center involved in the study. The cohort is built with patients diagnosed with glioblastoma (GBM) with a Magnetic Resonance Imaging (MRI) pre-treatment since 1st of January of 2012 until the Study Start Date.
The main objective of the study is to determine if the habitats obtained by the Hemodynamic Multiparametric Tissue Signature (HTS) biomarker, which describe the tumor vascular heterogeneity of the enhancing tumor and edema areas, are predictive of the overall survival of patients undergoing standard-of-care treatment.
The specific objectives of the study are:
Cox regression, Kaplan-Meier estimator and multiple linear regression analysis will be used to assess survival significance of each biomarker at each HTS habitat. The predictive value will be compared with models based on clinical and volumetric image variables: Age, Karnofsky Performance Status (KPS) Scale and Visually AcceSAble Rembrandt Images (VASARI) features. Moreover, the HTS-based models will be compared to models based on hemodynamic biomarkers, such as Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV), capillary permeability (Ktrans) and fractional Volume of Extravascular-Extracellular space (Ve), and diffusion biomarkers, such as Apparent Diffusion Coefficient (ADC), extracted from automatic segmentations of the edema and the enhancing tumor. Finally, Sørensen-Dice coefficient will be used to measure the correlation between MTS habitats in longitudinal studies.
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| Measure | Description | Time Frame |
|---|---|---|
| Correlation between overall survival (in days) of patients undergoing standard-of-care treatment and the tumor vascular heterogeneity described by the four habitats obtained by the Hemodynamic Multiparametric Tissue Signature (HTS) biomarker | The overall survival for each patient is estimated since the date of the preoperative Magnetic Resonance Imaging (MRI) to the exitus date. Exitus date will be collected from clinical records and should be confirmed by the main investigator from each center. | From the date of the first MRI acquisition until the date of death from any cause, assessed up to 80 months |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation between progression-free survival (in days) of patients undergoing standard-of-care treatment and the tumor vascular heterogeneity described by the four habitats obtained by the HTS biomarker | The progression-free survival for each patient is estimated since the date of the preoperative MRI to the date of recurrence. | From the date of the first MRI acquisition until the date of first documented progression, assessed up to 80 months |
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Inclusion Criteria:
Exclusion Criteria:
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The population of the target group is formed by patients diagnosed with Glioblastoma grade IV World Health Organization (WHO) with histopathological confirmation
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| Name | Affiliation | Role |
|---|---|---|
| Juan M Garcia Gomez, PhD | Universitat Politècnica de València | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universitat Politècnica de València | Valencia | 46022 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29357274 | Background | Juan-Albarracin J, Fuster-Garcia E, Perez-Girbes A, Aparici-Robles F, Alberich-Bayarri A, Revert-Ventura A, Marti-Bonmati L, Garcia-Gomez JM. Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. Radiology. 2018 Jun;287(3):944-954. doi: 10.1148/radiol.2017170845. Epub 2018 Jan 19. | |
| 25978453 |
| Label | URL |
|---|---|
| In order to allow the scientific community to test the biomarker, a non-commercial research purposes platform has been created. It offers the opportunity to upload cases of glioblastoma on which different services can be applied. | View source |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jul 19, 2016 | Mar 1, 2018 | Prot_SAP_001.pdf |
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| ID | Term |
|---|---|
| D005909 | Glioblastoma |
| ID | Term |
|---|---|
| D001254 | Astrocytoma |
| D005910 | Glioma |
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
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| OTHER |
| Hospital Clinic of Barcelona | OTHER |
| Azienda Ospedaliero-Universitaria di Parma | OTHER |
| Oslo University Hospital | OTHER |
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| Correlation between MTS habitats in longitudinal studies | In order to study this outcome, the postradiotherapy and the follow-up images in combination with the preoperative ones, will be used. | From the date of the first MRI acquisition until the date of death from any cause, assessed up to 80 months |
| Background |
| Juan-Albarracin J, Fuster-Garcia E, Manjon JV, Robles M, Aparici F, Marti-Bonmati L, Garcia-Gomez JM. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One. 2015 May 15;10(5):e0125143. doi: 10.1371/journal.pone.0125143. eCollection 2015. |
| 31654541 | Derived | Del Mar Alvarez-Torres M, Juan-Albarracin J, Fuster-Garcia E, Bellvis-Bataller F, Lorente D, Reynes G, Font de Mora J, Aparici-Robles F, Botella C, Munoz-Langa J, Faubel R, Asensio-Cuesta S, Garcia-Ferrando GA, Chelebian E, Auger C, Pineda J, Rovira A, Oleaga L, Molla-Olmos E, Revert AJ, Tshibanda L, Crisi G, Emblem KE, Martin D, Due-Tonnessen P, Meling TR, Filice S, Saez C, Garcia-Gomez JM. Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study. J Magn Reson Imaging. 2020 May;51(5):1478-1486. doi: 10.1002/jmri.26958. Epub 2019 Oct 26. |
| D009373 |
| Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |