Keywords: Liver, Machine Learning/Artificial Intelligence, Elastography, MR Value, Radiomics, RelaxometryMR elastography is currently the most accurate non-invasive diagnostic method for liver fibrosis. However, other pathologic processes co-existing with liver fibrosis influence the stiffness measurement and including other MR-based measures might improve fibrosis assessment. In this work we suggest to add texture analysis features calculated on liver T2 maps together with T2 values from fast radial turbo-spin-echo sequence into a machine-learning classification model and to compare the performance of the model after adding selected parameters. Our results show that including both, texture analysis and T2 values significantly improves the classification performance of the model.
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