Determination of reproducible radiomic features for diagnosis of fatty liver disease in a crab-eating macaque model

Abstract

Evaluation of the intra-subject reproducibility of radiomic features is pivotal but challenging because it requires multiple replicate measurements, typically lacking in the clinical setting. Radiomics analysis based on computed tomography (CT) has been increasingly used to characterize liver malignancies and liver diffusive diseases. However, radiomic features are greatly affected by scanning parameters and reconstruction kernels, among other factors. In this study, we examined the effects of diets, reconstruction kernels, and liver-to-spleen normalization on the intra-subject reproducibility of radiomic features. The final goal of this work is to create a framework that may help identify reproducible radiomics features suitable for further diagnosis and grading of fatty liver disease in nonhuman primates using radiomics analysis. As a first step, the identification of reproducible features is essential. To accomplish this aim, we retrospectively analyzed serial CT images from two groups of crab-eating macaques, fed a normal or atherogenic diet. Serial CT examinations resulted in 45 highresolution scans. From each scan, two CT images were reconstructed using a standard B kernel and a bone-enhanced D kernel, with and without normalization relative to the spleen. Radiomic features were extracted from six regions in the liver parenchyma. Intra-subject variability showed that many features are fully reproducible regardless of liver disease status whereas others are significantly different in a limited number of tests. Features significantly different between the normal and atherogenic diet groups were also investigated. Reproducible features were listed, with normalized images having more reproducible features.

Repeatability of computed tomography liver radiomic features in a nonhuman primate model of diet-induced steatosis

“…Preliminary results have been previously published in a SPIE Medical Imaging conference paper. 19 2 Methodology…”