Accuracy of magnetic resonance imaging diagnosis and grading of gliomas

Author:

Shukla Dhanwantari,Chandankhede Abhijeet R.,Sahoo Prafulla K.

Abstract

Background: Low- and high-grade gliomas differ in clinical presentation, natural history, treatment outcome, prognosis, survival pattern, histopathological, immunohistochemical and biomolecular profiles. Accurate pre-operative prediction of histopathological grade of gliomas remains challenging, and is critical for making optimum management plan and prognosticating the disease beforehand to determine the most cost-effective therapeutic choice with the best patient outcome. This prospective observational study on 54 patients aims to determine accuracy of pre-operative magnetic resonance imaging (MRI) in diagnosing and grading gliomas.Methods: Pre-operative grading of MRI-suspected gliomas was done by assigning scores of 0-2 to 9 criteria – midline crossing, perilesional edema, signal heterogeneity, intra-tumoral hemorrhage, tumor border definition, cystic/necrotic changes, mass effect, contrast enhancement and diffusion restriction. Total scores of 0-5, 6-9 and 10-18 were considered radiologically low, intermediate and high grades respectively and correlation with World Health Organization (WHO) grades I+II, III and IV respectively was determined.Results: MRI diagnosed 85.18% gliomas correctly. Pre-operative MR grading was 76-89% sensitive and 86-96% specific in predicting the histopathological grade of the gliomas. Signal heterogeneity and contrast enhancement had the highest whereas midline crossing and mass effect had the lowest correlation with histopathological grade.Conclusions: Pre-operative MRI is highly specific and somewhat less sensitive tool for grading gliomas pre-operatively. The diagnostic yield is higher for LGGs and GBMs, compared to anaplastic gliomas, probably due to their mixed or intermediate features. 

Publisher

Medip Academy

Subject

General Energy

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing the Prediction of Gliomas Disease with an Optimistic Ensemble Approach;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14

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