Support Vector Machine Classification of Brain Metastasis and Radiation Necrosis Based on Texture Analysis in MRI

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


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Area Under Curve
  • Brain / pathology*
  • Brain Neoplasms / diagnosis*
  • Brain Neoplasms / secondary*
  • Contrast Media
  • Diagnosis, Differential
  • Female
  • Humans
  • Image Enhancement
  • Magnetic Resonance Imaging*
  • Male
  • Middle Aged
  • Necrosis
  • Radiation Injuries / pathology*
  • Reproducibility of Results
  • Retrospective Studies
  • Support Vector Machine*


  • Contrast Media