ORIGINAL RESEARCH Support Vector Machine Classification of Brain Metastasis and Radiation Necrosis Based on Texture Analysis in MRI Andr es Larroza, MS, 1 David Moratal, PhD, 2 * Alexandra Paredes-S anchez, MS, 2 Emilio Soria-Olivas, PhD, 3 Mar  ıa L. Chust, MD, 4 Leoncio A. Arribas, MD, PhD, 4 and Estanislao Arana, MD, PhD 5 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 fea- tures 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 6 standard deviation) of 0.94 6 0.07 in the first case, and 0.93 6 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;42:1362–1368. B rain Metastases are the most common brain tumors and their treatment usually comprise a combination of sur- gery, radiosurgery, radiotherapy, and chemotherapy. 1 Stereo- tactic radiosurgery (SRS) for brain metastases may lead to delayed radiation necrosis, with symptoms and imaging findings usually indistinguishable from persistent tumor. 2–4 Brain metastases show heterogeneous imaging findings regardless their origin and therapy. 5 Multiparametric MRI and positron emission tomogra- phy (PET) have been advocated to differentiate metastatic recurrence from radiation-induced changes. 4,6,7 Even so, accurate discrimination remains difficult compared with his- topathology. 2,7 To avoid invasive diagnosis and due to incomplete availability of advanced imaging such as MR spectroscopy, perfusion MRI and PET, great interest exists in identifying reliable imaging features from routine MRI that could differentiate metastasis from radiation necrosis. 2,8 Texture analysis describes a wide range of techniques that enable the quantification of gray-level patterns, pixel interrelationships, and the spectral properties within an image; to derive features that provide a measure of intrale- sional heterogeneity. 9 Texture analysis has been applied in MRI for classification of tumors and other diseases. 10–15 Different tumor areas exhibit different textural patterns, which are beyond human visual perception. 16 The vast variety of texture analysis methods makes possible to obtain a myriad of features that can be used in combination with machine learning techniques to obtain a reliable diagnostic tool.. 17,18 The purpose of the present work was to develop a classification model using texture View this article online at wileyonlinelibrary.com. DOI: 10.1002/jmri.24913 Received Jan 23, 2015, and in revised form Mar 26, 2015. Accepted for publication Mar 26, 2015. *Address reprint requests to: D.M., Center for Biomaterials and Tissue Engineering, Universitat Polite ` cnica de Vale ` ncia, Cam  ı de Vera, s/n. 46022, Valencia, Spain. E-mail: dmoratal@eln.upv.es From the 1 Department of Medicine, Universitat de Vale ` ncia, Valencia, Spain; 2 Centre for Biomaterials and Tissue Engineering, Universitat Polite ` cnica de Vale ` ncia, Valencia, Spain; 3 Intelligent Data Analysis Laboratory, Electronic Engineering Department, Universitat de Vale ` ncia, Valencia, Spain; 4 Department of Radiation Oncology., Fundaci  on Instituto Valenciano de Oncolog  ıa, Valencia, Spain; and 5 Department of Radiology, Fundaci  on Instituto Valenciano de Oncolog  ıa, Valencia, Spain 1362 V C 2015 Wiley Periodicals, Inc.