Abstract
Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10–100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.
Highlights
In the past years the computed tomography (CT) field of interest has moved from qualitative to quantitative imaging, especially in oncology scenarios where heterogeneity within solid tumors is usually associated with malignant biology [1].Among CT quantitative methods explored, CT texture analysis (CTTA) is an area of radiomics that allows an ultrastructural quantitative evaluation by analyzing pixel or voxel grey levels of the image, reflecting the heterogeneity related to the biologic microenvironment [2]
We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features
This aspect is gaining interest, as shown by Erdal et al [11] who demonstrated that slice thickness influenced reproducibility of radiomic features in lung nodules, and Prezzi et al [12] who showed the influence of iterative reconstruction (IR) algorithm versus traditional filtered back projection (FBP) on radiomics quantification in twenty-eight datasets of colorectal cancer
Summary
In the past years the CT field of interest has moved from qualitative to quantitative imaging, especially in oncology scenarios where heterogeneity within solid tumors is usually associated with malignant biology [1].Among CT quantitative methods explored, CT texture analysis (CTTA) is an area of radiomics that allows an ultrastructural quantitative evaluation by analyzing pixel or voxel grey levels of the image, reflecting the heterogeneity related to the biologic microenvironment [2]. An important aspect is the CTTA reproducibility related to the influence of CT acquisition parameters (e.g., level of radiation dose, slice thickness, reconstruction algorithms) that can affect results and standardization among different studies [8,9,10]. This aspect is gaining interest, as shown by Erdal et al [11] who demonstrated that slice thickness influenced reproducibility of radiomic features in lung nodules, and Prezzi et al [12] who showed the influence of iterative reconstruction (IR) algorithm versus traditional filtered back projection (FBP) on radiomics quantification in twenty-eight datasets of colorectal cancer
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