Magnetic resonance imaging
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Multicenter Study Comparative Study
Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of multi-paramedic MRI. ⋯ Machine learning using radiomics features derived from multi-parametric MRI is capable of high AUC scores with high sensitivity and specificity in classifying meningiomas between low and higher gradings despite heterogeneous protocols across different centers. Feature selection can be performed effectively even when extracting a large amount of data for radiomics fingerprinting.