RESUMO
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.
