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

Suárez-García, José Gerardo ; Hernández-López, Javier Miguel ; Moreno-Barbosa, Eduardo ; de Celis-Alonso, Benito Sherman, Jonathan H

PloS one, 2020-05, Vol.15 (5), p.e0228972-e0228972 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
A simple model for glioma grading based on texture analysis applied to conventional brain MRI
Autor: Suárez-García, José Gerardo ; Hernández-López, Javier Miguel ; Moreno-Barbosa, Eduardo ; de Celis-Alonso, Benito
Sherman, Jonathan H
Assuntos: Aged ; Biopsy ; Brain ; Brain cancer ; Brain Neoplasms - classification ; Brain Neoplasms - diagnostic imaging ; Brain Neoplasms - pathology ; Brain tumors ; Cancer ; Cancer diagnosis ; Chemotherapy ; Classification ; Developing countries ; Diagnosis ; Diagnostic imaging ; Female ; Glioma ; Glioma - classification ; Glioma - diagnostic imaging ; Glioma - pathology ; Gliomas ; Health aspects ; Heterogeneity ; Humans ; Image contrast ; Image processing ; Information processing ; LDCs ; Life expectancy ; Low cost ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Magnetic Resonance Imaging - standards ; Male ; Mathematical models ; Mathematics ; Medical diagnosis ; Medical imaging ; Medical prognosis ; Medicine and Health Sciences ; Methods ; Middle Aged ; Model accuracy ; Models, Theoretical ; Neoplasm Grading - methods ; Neoplasm Grading - standards ; Neuroimaging ; Novels ; Patients ; Physical Sciences ; Physics ; Radiation therapy ; Research and Analysis Methods ; Sampling methods ; Sensitivity ; Texture ; Tumors
É parte de: PloS one, 2020-05, Vol.15 (5), p.e0228972-e0228972
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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

Suárez-García, José Gerardo ; Hernández-López, Javier Miguel ; Moreno-Barbosa, Eduardo ; de Celis-Alonso, Benito Sherman, Jonathan H

United States: Public Library of Science

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