ReviewTranslational cancer research2025
A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.
Review in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Background: Brain invasion plays an important role in determining the grade, prognosis, and recurrence of meningioma. There is an emerging trend to explore the feasibility of radiomics in prediction of brain invasion in meningiomas pre-operatively. This review aims to assess the role and quality of published magnetic resonance imaging (MRI) radiomics studies on brain invasion prediction. Methods: A search for literature published between January 2019 and October 2024 across five databases was conducted following the 2020 PRISMA guidelines. The methodological quality and risk of bias of eligible studies were assessed by the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results: Six of 381 studies were included in the final review. All the included studies were retrospective in nature. The majority (5/6, 83.3%) of studies analyzed multisequence MRI for radiomics analysis. Reported validation area under the curve (AUC) ranged from 0.74 to 0.91. The best-performing models described in the included studies used both radiomics and semantic features to construct the final feature signature. The overall methodological quality was low, with a mean radiomics quality score (RQS) score of 12.91 (35.86%). The overall risk of bias was low in 4/6 (66.6%), high in 1/6 (16.7%), and unclear in 1/6 (16.7%) studies. Conclusions: Radiomics analysis may have potential for predicting brain invasion in meningioma, but the current body of evidence and the overall methodological quality of studies are limited. Future studies should incorporate complementary information from multiparametric MRI, and prioritise robust external validation. Addressing class imbalance, and providing comprehensive evaluations of model performance and clinical utility will be essential to determine the practical value of radiomics models in practice.
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