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. 2024 Feb 14:14:1291861.
doi: 10.3389/fonc.2024.1291861. eCollection 2024.

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

Alexandru Garaba  1 , Nummra Aslam  1 , Francesco Ponzio  2 , Pier Paolo Panciani  1 , Waleed Brinjikji  3 , Marco Fontanella  1 , Lucio De Maria  1   4
Affiliations

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

Alexandru Garaba et al. Front Oncol. .

Abstract

Background and objective: 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, encompassing 12 studies and a total of 1779 patients, focusing 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 ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from 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 (ACC 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/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.

Keywords: gliomas; meta-analysis; primary central nervous system lymphomas; radiomics; systematic review.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
PRISMA flow diagram depicting the literature search process. PRISMA, preferred reporting items for systematic reviews and meta-analysis.
Figure 2
Figure 2
Forest plots with overall SEN and SPE for discrimination between CNS gliomas and PCNSLs. SEN, sensitivity; SPE, specificity; CNS, central nervous system; PCNSL, primary central nervous system lymphoma.
Figure 3
Figure 3
Endpoints of interest with individual confidence regions for differentiation between CNS gliomas and PCNSLs. CNS, central nervous system; PCNSL, primary central nervous system lymphoma.
Figure 4
Figure 4
Corresponding SROC curve for differentiation between CNS gliomas and PCNSLs. SROC, summary receiver operating characteristic; CNS, central nervous system; PCNSL, primary central nervous system lymphoma.
Figure 5
Figure 5
Funnel plots for the bivariate outcomes. (A) Se, sensitivity; (B) FPR, false positive rate.
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