Semiautomatic Classification of Benign Versus Malignant Vertebral Compression Fractures Using Texture and Gray-Level Features in Magnetic Resonance Images

Our study aimed to develop a system for computer-aided diagnosis of vertebral compression fractures (VCFs) using magnetic resonance imaging (MRI), to help in the differentiation between malignant and benign VCFs. Lumbar spine MRI was used to acquire T1-weighted images in the sagittal plane. Images from 63 consecutive patients (38 women, 25 men, mean age 62.25 ± 14.13 years) with at least one VCF diagnosis were studied. Contrast and texture features were extracted from manually segmented images of 103 vertebral bodies with VCFs. The classification of malignant vs. benign VCFs was performed using the k-nearest neighbor (KNN) classifier with the Euclidean distance. Using a KNN classifier with k=3, feature selection, and 10-fold cross-validation, we obtained a value of the area under the receiver operating characteristic curve of 0.913.