title: Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Medicine, Universitat de València, Valencia, Spain.
authors: Andrés
authors: A
authors: Larroza
authors: Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
authors: David
authors: D
authors: Moratal
authors: Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
authors: Alexandra
authors: A
authors: Paredes-Sánchez
authors: Intelligent Data Analysis Laboratory, Electronic Engineering Department, Universitat de València, Valencia, Spain.
authors: Emilio
authors: E
authors: Soria-Olivas
authors: Department of Radiation Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
authors: María L
authors: ML
authors: Chust
authors: Department of Radiation Oncology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
authors: Leoncio A
authors: LA
authors: Arribas
authors: Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
authors: Estanislao
authors: E
authors: Arana
mesh_terms: D019540:Area Under Curve; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000556:secondary*; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009336:Necrosis; D011832:Radiation Injuries / Q000473:pathology*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D060388:Support Vector Machine
chemical_list: D003287:Contrast Media
keywords: MRI; brain metastasis; classification; radiation necrosis; support vector machine; texture analysis