Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis

Abstract

Abstract Purpose: Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms. Methods: A systematic review of the literature and a meta-analysis were conducted, focusing on studies reporting on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test. Results: The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 – 0.91) and 87% (95% CI = 0.83 – 0.91), respectively. The best-performing features were the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (AUC 87% - 95%). Most studies applied a cross-validation analysis (92%). Conclusion: The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CTimaging modality is the best-performing, while the MRI CE-T1W is the most used.