NDLI: A simple model for glioma grading based on texture analysis applied to conventional brain MRI.

Content Provider	Directory of Open Access Journals (DOAJ)
Author	José Gerardo Suárez-García Javier Miguel Hernández-López Eduardo Moreno-Barbosa Benito de Celis-Alonso
Abstract	Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low-cost and easy-to-implement classification model which distinguishes low-grade gliomas (LGGs) from high-grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations of MRI contrasts (T1Gd and T2) and one segmented glioma region (necrotic and non-enhancing tumor core, NCR/NET) were studied. Texture features obtained from the gray level size zone matrix (GLSZM) were calculated. An under-sampling method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated, and the best model explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18%, respectively. According to the features of the model, when the NCR/NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T1Gd images, and LGGs had a more heterogeneous texture than HGGs in the T2 images. These novel results partially contrast with results from the literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that the heterogeneity of gliomas depended on the MRI contrast studied. The chosen model stands out as a simple, low-cost, easy-to-implement, reproducible and highly accurate glioma classifier. Importantly, it should be accessible to populations with reduced economic and scientific resources.
e-ISSN	19326203
DOI	10.1371/journal.pone.0228972
Journal	PLoS ONE
Issue Number	5
Volume Number	15
Language	English
Publisher	Public Library of Science (PLoS)
Publisher Date	2020-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Medicine Science
Content Type	Text
Resource Type	Article

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