Natural History of Meningiomas: Review with Meta-analyses.

growth curve growth rate incidental meningioma natural history volumetry

Journal

Neurologia medico-chirurgica
ISSN: 1349-8029
Titre abrégé: Neurol Med Chir (Tokyo)
Pays: Japan
ID NLM: 0400775

Informations de publication

Date de publication:
15 Mar 2020
Historique:
pubmed: 6 2 2020
medline: 3 2 2021
entrez: 4 2 2020
Statut: ppublish

Résumé

Observation has been a mainstay in asymptomatic meningiomas, but it may increase the risk associated with treatment due to tumor enlargement and the aging of patients. Understanding the natural course of meningiomas is important to provide appropriate treatment. The majority of previous studies investigated factors related to their growth, but failed to demonstrate their relationship with symptomatic progression (sympP) because of its rarity. We reviewed and meta-analyzed 27 studies that investigated natural courses in asymptomatic or untreated meningiomas to find clinico-radiological factors predictive of radiological progression (radioP), growth speed, and sympP. In results of time-growth analysis, two-thirds of meningiomas showed radioP defined by a volume criterion and the rate approached a plateau at 4-5 years. In growth curve analyses, about half of incidental meningiomas presented decelerating or no growth, while less than one-quarter of them grew exponentially. RadioP, growth speed [annual volume change (AVC) or relative growth rate], and sympP each had different factors related to them. Younger age, non-calcification, and high intensity on T2-weighted image were related to radioP and rapid growth speed, but not to sympP. Tumors in males and those of larger size were likely to be symptomatic in the meta-analysis. AVC (≥2.1 cm

Identifiants

pubmed: 32009127
doi: 10.2176/nmc.ra.2019-0213
pmc: PMC7073704
doi:

Types de publication

Journal Article Meta-Analysis Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

109-120

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Auteurs

Satoshi Nakasu (S)

Division of Neurosurgery, Kusatsu General Hospital.

Yoko Nakasu (Y)

Department of Neurosurgery, Shiga University of Medical Science.

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Classifications MeSH