The Effect of Ultrasound Image Pre-Processing on Radiomics Feature Quality: A Study on Shoulder Ultrasound

Abstract

Radiomics, the extraction of quantitative features from medical images, has shown great promise in enhancing diagnostic and prognostic models, particularly in CT and MRI. However, its application in ultrasound (US) imaging, especially in musculoskeletal (MSK) imaging, remains underexplored. The inherent variability of ultrasound, influenced by operator dependency and various imaging settings, presents significant challenges to the reproducibility of radiomic features. This study aims to identify whether commonly used image pre-processing methods can increase the reproducibility of radiomics features, increasing the quality of analysis. This is performed with shoulder calcific tendinopathy as a case study. Ultrasound images from 84 patients with rotator cuff calcifications were retrospectively analysed. Three pre-processing techniques-Histogram Equalization, Standard CLAHE, and Advanced CLAHE-were applied to adjust image quality. Manual segmentation of calcifications was performed, followed by the extraction of 849 radiomic features. The reproducibility of these features was assessed using the intraclass correlation coefficient (ICC), comparing results across pre-processing methods within the dataset. The Advanced CLAHE pre-processing method consistently yielded the highest ICC values, indicating superior reproducibility of radiomic features compared to other methods. Wavelet-transformed features, particularly in the GLCM and GLRLM subgroups, demonstrated robust reproducibility across all pre-processing techniques. Shape features, however, continued to show lower reproducibility. Advanced CLAHE pre-processing significantly enhances the reproducibility of radiomic features in ultrasound imaging of calcifications. This study underscores the importance of pre-processing in achieving reliable radiomic analyses, particularly in operator-dependent imaging modalities like ultrasound.

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