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

Autor: Javier Miguel Hernández-López, Benito de Celis-Alonso, E. Moreno-Barbosa, José Gerardo Suárez-García
Rok vydání: 2020
Předmět:
Male
Computer science
Cancer Treatment
Diagnostic Radiology
030218 nuclear medicine & medical imaging
Database and Informatics Methods
Mathematical and Statistical Techniques
0302 clinical medicine
Medicine and Health Sciences
Brain mri
Radiation treatment planning
Neurological Tumors
Mathematics
Multidisciplinary
medicine.diagnostic_test
Brain Neoplasms
Radiology and Imaging
Statistics
Glioma
Middle Aged
Magnetic Resonance Imaging
Cancer treatment
Gray level
Glioma grading
Oncology
Neurology
Physical Sciences
Medicine
Regression Analysis
Female
Research Article
Imaging Techniques
Science
Image Analysis
Linear Regression Analysis
Research and Analysis Methods
03 medical and health sciences
Text mining
Diagnostic Medicine
Linear regression
Cancer Detection and Diagnosis
medicine
Humans
Statistical Methods
Aged
business.industry
Cancers and Neoplasms
Magnetic resonance imaging
Pattern recognition
Models
Theoretical
medicine.disease
Artificial intelligence
Neoplasm Grading
business
030217 neurology & neurosurgery
Zdroj: PLoS ONE
PLoS ONE, Vol 15, Iss 5, p e0228972 (2020)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0228972
Popis: 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 between MRI contrasts (T1Gdand 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-samplig 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. The best model was 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 T1Gdimages and LGGs had a more heterogeneous texture than HGGs in the T2images. These novel results partially contrast with results from literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that heterogeneity of gliomas depended on the studied MRI contrast. The model presented stands out as a simple, low cost, easy to implement, reproducible and highly accurate glioma classifier. What is more important, it should be accessible to populations with reduced economic and scientific resources.
Databáze: OpenAIRE
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