Litcius/Paper detail

Radiomics feature robustness as measured using an MRI phantom

Joonsang Lee, Angela Steinmann, Yao Ding, Hannah Lee, Constance A. Owens, Jihong Wang, Jinzhong Yang, D Followill, Rachel Ger, Dennis Mackin, Laurence E. Court

2021Scientific Reports101 citationsDOIOpen Access PDF

Abstract

Radiomics involves high-throughput extraction of large numbers of quantitative features from medical images and analysis of these features to predict patients' outcome and support clinical decision-making. However, radiomics features are sensitive to several factors, including scanning protocols. The purpose of this study was to investigate the robustness of magnetic resonance imaging (MRI) radiomics features with various MRI scanning protocol parameters and scanners using an MRI radiomics phantom. The variability of the radiomics features with different scanning parameters and repeatability measured using a test-retest scheme were evaluated using the coefficient of variation and intraclass correlation coefficient (ICC) for both T1- and T2-weighted images. For variability measures, the features were categorized into three groups: large, intermediate, and small variation. For repeatability measures, the average T1- and T2-weighted image ICCs for the phantom (0.963 and 0.959, respectively) were higher than those for a healthy volunteer (0.856 and 0.849, respectively). Our results demonstrated that various radiomics features are dependent on different scanning parameters and scanners. The radiomics features with a low coefficient of variation and high ICC for both the phantom and volunteer can be considered good candidates for MRI radiomics studies. The results of this study will assist current and future MRI radiomics studies.

Topics & Concepts

RepeatabilityRadiomicsIntraclass correlationImaging phantomRobustness (evolution)Coefficient of variationMagnetic resonance imagingComputer scienceArtificial intelligenceMedicinePattern recognition (psychology)ReproducibilityNuclear medicineRadiologyMathematicsStatisticsBiochemistryGeneChemistryRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisSarcoma Diagnosis and Treatment