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

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.

[1]  Andrzej Materka,et al.  Texture analysis methodologies for magnetic resonance imaging , 2004, Dialogues in clinical neuroscience.

[2]  Peter A. Bandettini,et al.  Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images , 2012, NeuroImage.

[3]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[4]  M. Markey,et al.  Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. , 2013, Neuro-oncology.

[5]  Jan Sijbers,et al.  Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images , 2010, Journal of magnetic resonance imaging : JMRI.

[6]  Prateek Prasanna,et al.  Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI , 2014, Medical Imaging.

[7]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[8]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[9]  L. Brady,et al.  Radiation therapy for brain metastases. , 1987 .

[10]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[11]  G. Barnett,et al.  Conventional MRI does not reliably distinguish radiation necrosis from tumor recurrence after stereotactic radiosurgery , 2012, Journal of Neuro-Oncology.

[12]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[13]  Christos Davatzikos,et al.  Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.

[14]  F. Azuaje,et al.  Multiple SVM-RFE for gene selection in cancer classification with expression data , 2005, IEEE Transactions on NanoBioscience.

[15]  E Le Rumeur,et al.  MRI texture analysis on texture test objects, normal brain and intracranial tumors. , 2003, Magnetic resonance imaging.

[16]  Dimitri Van De Ville,et al.  Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities , 2014, Medical Image Anal..

[17]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[18]  M. House,et al.  Texture‐based classification of liver fibrosis using MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[19]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[20]  P. Sundgren,et al.  MR spectroscopy using normalized and non-normalized metabolite ratios for differentiating recurrent brain tumor from radiation injury. , 2011, Academic radiology.

[21]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[22]  Ronald M. Summers,et al.  Machine learning and radiology , 2012, Medical Image Anal..

[23]  J. Knisely,et al.  A Comprehensive Review of MR Imaging Changes following Radiosurgery to 500 Brain Metastases , 2011, American Journal of Neuroradiology.

[24]  M. Ruge,et al.  Differentiation of local tumor recurrence from radiation-induced changes after stereotactic radiosurgery for treatment of brain metastasis: case report and review of the literature , 2013, Radiation oncology.

[25]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[26]  Surjith Vattoth,et al.  Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence. , 2012, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  A. Drevelegas,et al.  Imaging of Brain Tumors with Histological Correlations , 2002, Springer Berlin Heidelberg.

[28]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[29]  Antonios Drevelegas Imaging Of Brain Tumors With Histological Correlations , 2002 .

[30]  Julien Milles,et al.  Texture analysis of ultrahigh field T2*‐weighted MR images of the brain: Application to Huntington's disease , 2014, Journal of magnetic resonance imaging : JMRI.

[31]  Shigeaki Higashiyama,et al.  Diagnostic Accuracy of 11C-Methionine PET for Differentiation of Recurrent Brain Tumors from Radiation Necrosis After Radiotherapy , 2008, Journal of Nuclear Medicine.

[32]  T. Mikkelsen,et al.  Treatment induced necrosis versus recurrent/progressing brain tumor: going beyond the boundaries of conventional morphologic imaging , 2010, Journal of Neuro-Oncology.

[33]  Glenn E. Shelinel,et al.  Radiation therapy for brain metastases , 2004, Journal of Neuro-Oncology.

[34]  Sean S. Park,et al.  Differentiation between intra-axial metastatic tumor progression and radiation injury following fractionated radiation therapy or stereotactic radiosurgery using MR spectroscopy, perfusion MR imaging or volume progression modeling. , 2011, Magnetic resonance imaging.

[35]  Samuel T Chao,et al.  Challenges with the diagnosis and treatment of cerebral radiation necrosis. , 2013, International journal of radiation oncology, biology, physics.

[36]  Peter Gibbs,et al.  Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.

[37]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[38]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[39]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[40]  A. Gamst,et al.  Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast‐enhanced magnetic resonance images , 2012, Journal of magnetic resonance imaging : JMRI.

[41]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.