Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy

Abstract

Abstract Background: Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients. Methods: This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC). Results: Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV. Conclusion: The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.