Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings

BACKGROUND: The aim of this study was to compare the ability of a standard magnetic resonance imaging (MRI)-based radiomics model and a semantic features logistic regression model in differentiating between predominantly osteolytic and osteoblastic spinal metastases. METHODS: We retrospectively analyzed standard MRIs and computed tomography (CT) images of 78 lesions of spinal metastases, of which 52 and 26 were predominantly osteolytic and osteoblastic, respectively. CT images were used as references for determining the sensitivity and specificity of standard MRI. Five standard MRI semantic features of each lesion were evaluated and used for constructing a logistic regression model to differentiate between predominantly osteolytic and osteoblastic metastases. For each lesion, 107 radiomics features were extracted. Six features were selected using a support vector machine (SVM) and were used for constructing classification models. Model performance was measured by means of the area under the curve (AUC) approach and compared using receiver operating characteristics (ROC) curve analysis. RESULTS: The signal intensity on T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed T2-weighted (FS-T2W) MRI sequences were significantly different between predominantly osteolytic and osteoblastic spinal metastases (P<0.001), as is the case with the existence of soft-tissue masses. The overall prediction accuracy of the models based on radiomics and semantic features was 78.2% and 75.6%, respectively, with corresponding AUCs of 0.82 and 0.79, respectively. CONCLUSIONS: The standard MRI-based radiomics model outperformed the semantic features logistic regression model with regard to differentiating predominantly osteolytic and osteoblastic spinal metastases.