Andrés Larroza1, David Moratal2, Alexandra Paredes-Sánchez2, Emilio Soria-Olivas3, María L Chust4, Leoncio A Arribas4, Estanislao Arana5. 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.
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.
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Authors: Monika Béresová; Andrés Larroza; Estanislao Arana; József Varga; László Balkay; David Moratal Journal: MAGMA Date: 2017-09-22 Impact factor: 2.310