Determine the Correlation of Clinicopathologic and Radiologic Characteristic Predicting the Outcome of Meningioma

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Determine the Correlation of Clinicopathologic and Radiologic Characteristic Predicting the Outcome of Meningioma

Journal Title: International Journal of Neurology and Neurosurgery - Year 2019, Vol 11, Issue 1

Abstract

Introduction: Meningiomas of WHO grade I can, display invasive growth [2], even though there is no histopathologic difference between invasive and non-invasive tumors. Lack of homogeneity in the WHO grade I meningioma group itself, which is histologically divided into different subtypes with variable biological behaviour [3], Intra-tumoral heterogeneity, which might be spatial (due to the genetic aberrations in the different geographical regions of the same tumor) or temporal (between the primary tumor and the local recurrence), has not been studied adequately in meningiomas [4]. It is necessary to develop a comprehensive scoring system which can predict outcome in meningioma patients based on CLASS Algorithm, Histopathology, Simpson Grading and Radiology characteristics Objective: To develop a comprehensive scheme to prognosticate and to predict recurrence for meningioma based on pre-operative CLASS algorithm assessment with Preoperative imaging, Intraoperative completeness of resection-Simpson grading- WHO Classification 2016. Materials and Methods 1. A retrospective study based on hospital records and patient follow up who were treated surgically in Institute of Neurosurgery Madras Medical College. 2. Fifty cases of meningioma that were treated surgically in our hospital between June 2017 and February 2018 were included in the study. 3. Computed tomography (CT) was performed before and after contrast administration in all cases. 4. Magnetic resonance imaging (MRI) including pre- and post-contrast T1- weighted imaging using spin-echo (SE) sequences and T2-weighted imaging using fast spin-echo (FSE) sequences was performed in all cases. 5. Early outcome at 6 weeks and late outcome at 12 months was assessed using the Glasgow outcome scale (GOS), and postoperative neurologic and medical complications were recorded. 6. Chi-square and Fisher’s exact test were used for the comparison of the groups. 7. A logistic regression model was built to compare each group in terms of the odds of having “bad” GOS (GOS 1–3) and neurologic/medical complications. 8. A p-value of 0.05 and below was accepted as statistically signi cant. Results: Out of 50 patients 27 (54%) were male, 23 (46%) female. Youngest patient age was 10 years and oldest was 75 years. Mean age presentation was 47.62 years; median age was around 48 years. Most common location being convexity followed by parasagittal and skull base regions. GROUP III patients of CLASS algorithm had the worst outcome The completeness of resection was a major predictor of outcome with better outcomes seen with greater extent of resection. Higher Grade according to WHO classification was associated with a worse outcome. However, it was not a predictor of recurrence. High Risk’ imaging characteristics had a significantly worse outcomes Conclusion: Our study was able to concur that these indicators when factored together can predict the outcome and disease-free survival. The WHO Grading and Simpson’s Grading while important predictors of outcome, by themselves fail to predict the chances of recurrence. CLASS HSR is a useful predictor of Recurrence and Outcome of patients with Meningioma.With the individual components all having significant correlation with outcome, the combination offers an advantage in predicting disease free survival period.

Authors and Affiliations

K. Sudhakar

Keywords

Meningioma; Who Classification 2016; Simpson Grading Class Algorithm.

EP ID EP653174
DOI 10.21088/ijnns.0975.0223.11119.7
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