Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.

Autor: Larroza A; Department of Medicine, Universitat de València, Valencia, Spain., Moratal D; Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain., Paredes-Sánchez A; Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain., Soria-Olivas E; Intelligent Data Analysis Laboratory, Electronic Engineering Department, Universitat de València, Valencia, Spain., Chust ML; Department of Radiation Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain., Arribas LA; Department of Radiation Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain., Arana E; Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
Jazyk: angličtina
Zdroj: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2015 Nov; Vol. 42 (5), pp. 1362-8. Date of Electronic Publication: 2015 Apr 10.
DOI: 10.1002/jmri.24913
Abstrakt: Purpose: To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis.
Methods: Texture features were extracted from 115 lesions: 32 of them previously diagnosed as radiation necrosis, 23 as radiation-treated metastasis and 60 untreated metastases; including a total of 179 features derived from six texture analysis methods. A feature selection technique based on support vector machine was used to obtain a subset of features that provide optimal performance.
Results: The highest classification accuracy evaluated over test sets was achieved with a subset of ten features when the untreated metastases were not considered; and with a subset of seven features when the classifier was trained with untreated metastases and tested on treated ones. Receiver operating characteristic curves provided area-under-the-curve (mean ± standard deviation) of 0.94 ± 0.07 in the first case, and 0.93 ± 0.02 in the second.
Conclusion: High classification accuracy (AUC > 0.9) was obtained using texture features and a support vector machine classifier in an approach based on conventional MRI to differentiate between brain metastasis and radiation necrosis.
(© 2015 Wiley Periodicals, Inc.)
Databáze: MEDLINE
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