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Macrovascular Networks on Contrast-Enhanced Magnetic Resonance Imaging Improves Survival Prediction in Newly Diagnosed Glioblastoma

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dc.contributor.author Puig, Josep
dc.contributor.author Capellades Font, Jaume
dc.contributor.author Pedraza, Salvador
dc.date.accessioned 2019-03-25T08:30:08Z
dc.date.available 2019-03-25T08:30:08Z
dc.date.issued 2019
dc.identifier.citation Puig J, Biarnés C, Daunis-I-Estadella P, Blasco G, Gimeno A, Essig M. et al. Macrovascular Networks on Contrast-Enhanced Magnetic Resonance Imaging Improves Survival Prediction in Newly Diagnosed Glioblastoma. Cancers (Basel). 2019 Jan 14;11(1). pii: E84. DOI: 10.3390/cancers11010084
dc.identifier.issn 2072-6694
dc.identifier.uri http://hdl.handle.net/10230/36951
dc.description.abstract A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volumeCEL, increased rCBFCEL, and poor survival; nVS correlated negatively with survival (r = -0.286; p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher MDPI
dc.relation.ispartof Cancers (Basel). 2019 Jan 14;11(1):E84
dc.rights © Puig J, Biarnés C, Daunis-I-Estadella P, Blasco G, Gimeno A, Essig M. et al. by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Ressonància magnètica
dc.subject.other Glioblastoma
dc.title Macrovascular Networks on Contrast-Enhanced Magnetic Resonance Imaging Improves Survival Prediction in Newly Diagnosed Glioblastoma
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3390/cancers11010084
dc.subject.keyword Angiogenesis
dc.subject.keyword Biomarker
dc.subject.keyword Glioblastoma
dc.subject.keyword Magnetic resonance imaging
dc.subject.keyword Survival
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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