Radiomics improves the prognosis assessment of glioma recurrences: Focus on reliability analysis of MRI features

Abstract

Purpose To investigate whether imaging biomarkers could improve the efficacy of recurrent glioma survival prediction compared with that of the established clinical factors model. Method The clinical information of 80 patients was recorded in detail along with the radiomic features of the tumor region on recurrent MR images. An overall survival (OS) prediction model was proposed that combines clinical information and radiomic features. To improve the model’s generalizability and reliability, three-level feature selection methods (Kruskal‒Wallis test, Pearson correlation coefficient, and LASSO) were utilized. Finally, feature maps were constructed to explain the radiomic features. Results Six radiomic features and three clinical factors were identified to have prognostic value for recurrent glioma. The model combining radiomics features and clinical factors achieved better predictive performance (C-index = 0.787) than the clinical-based model (C-index = 0.734). KM survival curves showed clear differences between the high- and low-risk OS groups, with C-indexes of 0.751 (p < .0001) and 0.687 (p = 0.018), respectively. Conclusion Radiomics features improve overall survival prediction for recurrent glioma patients.