Radiomic features are sensitive to changes in imaging parameters in MRI. This makes it challenging to develop robust machine learning models using imaging features. We explore the effect of clinically available deep learning image reconstruction on the performance of radiomic features. Correlation coefficient values varied (0.56 - 1.00) when comparing radiomic features of deep learning reconstructed images and ‘conventional’ MRI scans. The noise reduction level had a large impact on correlation coefficients, but variations were also significant between different types of imaging feature. Identification of highly correlated features may help identify more stable sets of radiomic features for machine learning.