The Common radiological features of Meningiomas on CT scan and MRI among patients at major Hospitals in Eldoret, Kenya

Abstract

Meningiomas are amongst the commonest primary brain tumours accounting for about 33% of all brain tumours. World Health Organization classifies Meningiomas into three grades based on histopathology; the subtype of which affects the prognosis. Imaging plays a key role in the diagnosis of Meningiomas and is often the first investigation aiding in its diagnosis. This paper examines the common radiological features of Meningiomas based on CT scan and MRI has seen among patients at the Moi Teaching and Referral, Mediheal and Eldoret hospitals in Kenya. The study was carried out in the Moi Teaching and Referral Hospital, Mediheal and Eldoret hospitals in Eldoret, Kenya. A cross-sectional study design was used. Radiopathological association has used CT scan and MRI images which had a confirmatory histopathology report. Fifty-five patients were studied from May 2008 to December 2012. The inclusion criterion was the presence of both histopathology and CT or MRI images while exclusion was where either lacked. Data analysis was done by use of STATA version 12. The common Meningiomas encountered were grade I (95%) with the meningothelial (53%), fibroblasts (22%) and transitional (20%) subtypes seen. Three grade II atypical Meningiomas were found but no malignant Meningiomas was encountered in the study population. The common CT scans features encountered were extra-axial, hyperdense (87%), mass lesions (98%) with mild (36%) to moderate edema (45%) that avidly enhanced with contrast either homogeneously (47%) or heterogeneously (53%). Common MRI features encountered were extra-axial mass lesions (97%) which were isointense (61%) on T1 weighted sequences, hyperintense (65 %) on T2 weighted images, hyperintense (65%) on FLAIR images and enhanced (100%) when gadolinium contrast was injected. Though imaging can reliably diagnose Meningiomas, histopathological subtypes of Meningiomas cannot be differentiated from each other based on radiological features.