Classification of glioma and solitary brain metastasis using magnetic resonance imaging radiomics feature / 解剖学报

ABSTRACT

Objective To analyze the difference of radiomics features between solitary brain metastasis and glioma using routine 3T TI, T2 and fluid attenuation inversion recovery (FLAIR) magnetic resonance imaging, to explore the significance of texture features constructed in different directions and angles in tumor regions in distinguishing the two kinds of tumors, and to explore a feasible method for high-precision classification of solitary brain metastases and gliomas. Methods Given the multimodal images of 43 patients with glioma and 45 age- and sex- matched patients with solitary brain metastasis, the gray level co-occurrence matrices of different angles of each slice were constructed from the transverse, coronal and sagittal directions of the tumor regions of these images, and the texture spatial relationship features (including contrast, correlation, energy and homogeneity) were calculated. Wilcoxon rank sum test was used to eliminate redundant features and select features with strong distinguishing ability. Finally, SVM linear kernel classifier was used to classify the selected features to achieve the identification of the two kinds of tumors. Results When classifying glioma and solitary brain metastasis, the precision, recall, Fl score and accuracy of multimodal and multidirectional combination features were 0.8857, 0.9114, 0.8944 and 0.8922, respectively. The area under the receiver operating characteristic curve obtained by linear kernel SVM classifier was 0. 9602. Totally 40 of the 45 patients with solitary brain metastases were correctly classified, and 39 of the 43 gliomas were correctly classified. Conclusion The multimodal and multi-directional combination features of tumor areas can be classified by linear kernel SVM classifier to distinguish gliomas from solitary brain metastases, which can be used as a second opinion to effectively assist doctors in making diagnosis.