Statistical analysis of radiomic features in differentiation of glioma grades

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Gokalp Cinarer
https://orcid.org/0000-0003-0818-6746
Bulent Gursel Emiroglu
https://orcid.org/0000-0002-1656-6450

Abstract

Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were tested by Spearman’s correlation analysis. Differences between Grade II and Grade III tumour categories were analysed with Mann–Whitney U test. According to the results, it was seen that radiomic features can be used to differentiate the features of tumour levels evaluated in the same category. These results show that by employing radiomic features brain cancer grade detection can help machine learning technologies and radiological analysis.


 


Keywords: Radiomics, glioma, image processing.

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How to Cite
Cinarer, G., & Emiroglu, B. (2020). Statistical analysis of radiomic features in differentiation of glioma grades. New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, (12), 68-79. https://doi.org/10.18844/gjpaas.v0i12.4988
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