Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach

Abstract

Detection of brain metastases in patients with undiagnosed primary cancer is unusual but still an existing phenomenon. In these cases, identifying the cancer site of origin is non-feasible by visual examination of magnetic resonance (MR) images. Recently, radiomics has been proposed to analyze differences among classes of visually imperceptible imaging characteristics. In this study we analyzed 46 T1-weighted MR images of brain metastases from 29 patients: 29 of lung and 17 of breast origin. A total of 43 radiomics texture features were extracted from the metastatic lesions. Support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers were implemented to evaluate the classification performance. The influence of gray-level quantization for computation of texture features was also examined. The best classification (AUC = 0.953 ± 0.061), evaluated with nested cross-validation, was obtained using the SVM classifier with two texture features derived from the 16 gray-level quantization co-occurrence matrix.