Proceedings Article | 10 April 2023
KEYWORDS: Radiomics, Cooccurrence matrices, Muscles, Breast, Ultrasonography, Feature extraction, Visualization, Visual analytics, Visibility, Radiation oncology
Ultrasound (US) radiomics analysis is an emerging research field to overcome clinician’s subjectivity of visual image assessment and interpretation. However, its clinical utility is still limited and the efficacy depends on the robustness of radiomic features. The purpose of this work is to evaluate the robustness of US radiomic features with various scanning settings, including central frequency, focal length, and overall Brightness Gain (BG). We tested the concept with Grey Level Co-occurrence Matrix (GLCM) features. All US images were acquired using a Hitachi Noblus US system and a bi-plane probe (EUP-U533C). The study utilized three materials: a tissue-mimicking phantom, beef muscle, and chicken breast. A total of 21 GLCM features were extracted from the US images. The relative percentage change was calculated as the standard deviation (STDEV) or the maximum difference (max difference) divided by the absolute mean value of each GLCM feature, varying in the BG = 19-29 zone. Among the 21 extracted GLCM features, we found seven robust features, namely differenceEntropy, entropy, homogeneity1, IDMN, IDN, inverseVariance, and sumEntropy, enduring within 10% variances when varying frequency, focal length, and BG settings. The results of this study indicate that some US radiomic features may be affected by scanning parameters, while others are more robust to these variations. As radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future, the robust features should be carefully chosen to obtain reliable radiomics-based analysis.
