ArticleJournal of neuro-oncology2025
Novel predictors of tumor growth by exploratory quantitative analysis of radiomics features from MRI data for incidentally discovered meningioma.
Article in Journal of neuro-oncology, 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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Abstract
purposePredicting future tumor growth from initial imaging of incidentally discovered meningioma (IDM) could inform treatment decisions. However, most factors identified in prior studies on meningioma growth are qualitative. The aim of this study is to identify factors associated with tumor growth using quantitative radiomics features from MRI data.
methodsMRI T2 features from initial imaging of 24 tumor growth cases were compared with those of 25 cases without growth. An in-house program was developed to reduce the time required for data analysis. This program is based on the open-source software 3D Slicer 5.6.2 and PyRadiomics 3.1.0. It enables semi-automatic batch t-test analyses for each feature to compare tumor growth and non-growth groups. Regions of interest (ROIs) were placed in the tumor, outer tumor edge, whole brain, and white matter contralateral to the tumor. A total of 107 features were analyzed across seven classifications: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighboring Gray Tone Difference Matrix. A t-test was used to identify significant predictors.
resultsTen features across five classifications showed significant differences (p < 0.05): 2 First Order statistics, 2 Shape features, 4 Gy Level Co-occurrence Matrices, 1 Gy Level Size Zone Matrix, and 1 Neighboring Gray Tone Difference Matrix.
conclusionsPotential predictors of IDM growth were identified using radiomics features. Future studies with larger cohorts and validation will be essential to confirm the clinical utility and improve the predictive accuracy of these features.
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