Radiomics feature robustness as measured using an MRI phantom.
Joonsang LeeAngela SteinmannYao DingHannah LeeConstance OwensJihong WangJinzhong YangDavid FollowillRachel B GerDennis M MackinLaurence E CourtPublished in: Scientific reports (2021)
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.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- diffusion weighted imaging
- computed tomography
- magnetic resonance
- lymph node metastasis
- dual energy
- high throughput
- high resolution
- deep learning
- decision making
- newly diagnosed
- healthcare
- image quality
- machine learning
- squamous cell carcinoma
- ejection fraction
- prognostic factors
- optical coherence tomography