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RETROSPECTIVE EVALUATION OF MRI PATTERN OF GLIOBLASTOMA IN A TERTIARY HOSPITAL IN NIGERIA. | LitMetric

Introduction: Malignant gliomas, especially glioblastomas, are among the most aggressive and devastating of cancers, commonly producing profound progressive disability and leading to death in most cases. Conventional magnetic resonance (MR) imaging with gadolinium-based contrast agents is the most widely established and most useful tool in the characterization of cerebral tumors including Glioblastomas. This study aims to describe the imaging characteristics of Glioblastoma in African patients using conventional MR imaging.

Methodology: This was a retrospective cross-sectional study carried out at a Nigerian tertiary hospital. The demographic data, MR images and reports of patients with imaging and histological diagnosis of Glioblastoma between January 2003 and September 2017 were retrieved and reviewed. All the recorded data were analyzed using simple proportion and descriptive statistics with the Statistical Package for Social Sciences (SPSS) version 20.0 software for Windows.

Results: One hundred and twenty-two (122) patients had brain tumors during the review period, out of which 14 (11.5%) had histologically confirmed glioblastoma. The male- to -female ratio was 2.5 to 1.0. The age ranged between 14 and 72 years with a mean age of 49.6 years SD ±16.3. Twelve (85.7%) patients had solitary tumors and 2 (14.3%) had multiple tumors. Six (42.9%) were found on the right hemisphere only, 5 (35.7%) were found on the left hemisphere while 3 (21.4%) traversed both hemispheres. All tumors showed inhomogeneous enhancement and significant midline shift to the contra-lateral side of greater than 3mm. Only 1 (7.1%) tumor showed evidence of intra-tumoral bleed detected on T2* sequence.

Conclusion: Glioblastoma is a known aggressive brain tumor with unique MR imaging characteristics. While midline shift is typical, intra-tumoral bleeding may be an uncommon finding at presentation in our center.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10811701PMC

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