BBAuthors: Sun, P.; Advisor: -; Participants: Wang, D.; Mok, V. C.; Shi, L. (2019)
Radiomics-based researches have shown predictive abilities with machine-learning approaches. However, it is still unknown whether different radiomics strategies affect the prediction performance. The aim of this study was to compare the prediction performance of frequently utilized radiomics feature selection and classi cation methods in glioma grading. Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects. Then, the diagnostic performance of sixteen feature selection and fteen classi cation methods were evaluated by using two different test modes: ten-fold cross-validation and percentage split. Balanced... 
