Open AccessStatistical analysis of radiomic features in differentiation of glioma grades
Author(s) -
Gökalp Çınarer,
Bülent Gürsel Emiroğlu
Publication year - 2020
Publication title -
new trends and issues proceedings on advances in pure and applied sciences
Language(s) - English
Resource type - Journals
ISSN - 2547-880X
DOI - 10.18844/gjpaas.v0i12.4988
Subject(s) - radiomics , glioma , tumor grade , mann–whitney u test , correlation , artificial intelligence , medicine , pattern recognition (psychology) , computer science , cancer , mathematics , geometry , cancer research
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.