Effect of tube current on computed tomography radiomic features

Abstract

Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5). To gauge the size of the tube current effects, we scaled the features by the coefficient of variation of the corresponding features extracted from images of non-small cell lung cancer tumors. Variations in the tube current had more effect on features extracted from homogeneous materials (acrylic, sycamore wood) than from materials with more tissue-like textures (cork, rubber particles). Thirty-eight of the 48 features extracted from acrylic were affected by current reductions compared with only 2 of the 48 features extracted from rubber particles. These results indicate that variable x-ray tube current is unlikely to have a large effect on radiomic features extracted from computed tomography images of textured objects such as tumors.

Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

“…However, subgroup analysis results indicated that the CT acquisition period and exposure value did not affect the radiomic features. Mackin et al investigated the effect of CT tube currents on radiomic features using the Credence Cartridge Radiomics Phantom [37]; they concluded that the variable tube current did not have a large effect on the radiomic features, in agreement with our results.…”

Optimal Statistical Incorporation of Independent Feature Stability Information into Radiomics Studies

“…Recent research has shown that some confounding variables have a greater impact on feature stability. For example, the placement of a region of interest has a bigger effect than imaging parameters 7,9 , and this has been validated by our experiments. However, the actual influence of a confounding variable on a specific feature depends on many parameters such as imaging modality, imaging parameters, the cancer entity, or the feature family.…”

“…The values of a radiomics feature can depend on many different variables, not solely on the desired correlation with clinically relevant parameters or outcome [7][8][9][10][11] , but also on variables such as imaging parameters. Imaging parameters such as resolution, presence or absence of artefacts, the actual imaging device used, the reconstruction algorithm for computed tomography (CT) images, or the sequence for magnetic resonance imaging (MRI) are some examples of possible influences.…”

Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters

“…Several studies have shown that various conditions affecting image quality significantly influence the reliability of RF measurements. For example, inconsistencies across CT scanners such as, spatial resolution, tube current, noise, and reconstruction algorithm may decrease the reliability of CT-derived RFs [9][10][11][12][13][14] . The reliability of RFs also depends on the techniques used for lesion segmentation, grey-level discretization and quantization range 8,[15][16][17][18] .…”