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J Magn Reson Imaging. 2015 Nov;42(5):1362-8. doi: 10.1002/jmri.24913. Epub 2015 Apr 10.

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

Author information

1
Department of Medicine, Universitat de València, Valencia, Spain.
2
Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
3
Intelligent Data Analysis Laboratory, Electronic Engineering Department, Universitat de València, Valencia, Spain.
4
Department of Radiation Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
5
Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.

Abstract

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.

KEYWORDS:

MRI; brain metastasis; classification; radiation necrosis; support vector machine; texture analysis

PMID:
25865833
DOI:
10.1002/jmri.24913
[Indexed for MEDLINE]

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