Larroza Andrés, Moratal David, Paredes-Sánchez Alexandra, Soria-Olivas Emilio, Chust María L, Arribas Leoncio A, Arana Estanislao
Department of Medicine, Universitat de València, Valencia, Spain.
Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
J Magn Reson Imaging. 2015 Nov;42(5):1362-8. doi: 10.1002/jmri.24913. Epub 2015 Apr 10.
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.
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.
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.
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.
利用增强T1加权图像中的纹理特征和支持向量机开发一种分类模型，以区分脑转移瘤和放射性坏死。
从115个病变中提取纹理特征，其中32个先前诊断为放射性坏死，23个为放射治疗后的转移瘤，60个为未治疗的转移瘤；共包括来自六种纹理分析方法的179个特征。使用基于支持向量机的特征选择技术来获得提供最佳性能的特征子集。
当不考虑未治疗的转移瘤时，使用十个特征的子集在测试集上评估得到最高分类准确率；当分类器用未治疗的转移瘤训练并在治疗后的转移瘤上测试时，使用七个特征的子集。在第一种情况下，受试者工作特征曲线的曲线下面积（平均值±标准差）为0.94±0.07，在第二种情况下为0.93±0.02。
在基于传统MRI的方法中，利用纹理特征和支持向量机分类器区分脑转移瘤和放射性坏死，获得了较高的分类准确率（AUC>0.9）。
