Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups.
Journal
AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
25
09
2018
accepted:
25
02
2019
pubmed:
30
3
2019
medline:
17
3
2020
entrez:
30
3
2019
Statut:
ppublish
Résumé
Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. Age ( A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.
Sections du résumé
BACKGROUND AND PURPOSE
Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously.
MATERIALS AND METHODS
A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis.
RESULTS
Age (
CONCLUSIONS
A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.
Identifiants
pubmed: 30923085
pii: ajnr.A6019
doi: 10.3174/ajnr.A6019
pmc: PMC7048517
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
634-640Informations de copyright
© 2019 by American Journal of Neuroradiology.
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