Premium
Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI
Author(s) -
Larroza Andrés,
Moratal David,
ParedesSánchez Alexandra,
SoriaOlivas Emilio,
Chust María L.,
Arribas Leoncio A.,
Arana Estanislao
Publication year - 2015
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24913
Subject(s) - support vector machine , receiver operating characteristic , artificial intelligence , pattern recognition (psychology) , classifier (uml) , brain metastasis , metastasis , necrosis , standard deviation , feature selection , computer science , magnetic resonance imaging , feature vector , medicine , radiology , nuclear medicine , pathology , mathematics , cancer , machine learning , statistics
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. J. Magn. Reson. Imaging 2015. J. Magn. Reson. Imaging 2015;42:1362–1368.