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. 2023 Nov;52(8):20230180.
doi: 10.1259/dmfr.20230180. Epub 2023 Oct 23.

Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study

Xian He  1 , Zhi Chen  2 , Yutao Gao  3 , Wanjing Wang  4 , Meng You  5
Affiliations

Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study

Xian He et al. Dentomaxillofac Radiol. 2023 Nov.

Abstract

Objectives: This study aims to determine the reproducibility and location-stability of cone-beam computed tomography (CBCT) radiomic features.

Methods: Centrifugal tubes with six concentrations of K2HPO4 solutions (50, 100, 200, 400, 600, and 800 mg ml-1) were imaged within a customized phantom. For each concentration, images were captured twice as test and retest sets. Totally, 69 radiomic features were extracted by LIFEx. The reproducibility was assessed between the test and retest sets. We used the concordance correlation coefficient (CCC) to screen qualified features and then compared the differences in the numbers of them under 24 series (four locations groups * six concentrations). The location-stability was assessed using the Kruskal-Wallis test under different concentration sets; likewise, the numbers of qualified features under six test sets were analyzed.

Results: There were 20 and 23 qualified features in the reproducibility and location-stability experiments, respectively. In the reproducibility experiment, the performance of the peripheral groups and high-concentration sets was significantly better than the center groups and low-concentration sets. The effect of concentration on the location-stability of features was not monotonic, and the number of qualified features in the low-concentration sets was greater than that in the high-concentration sets. No features were qualified in both experiments.

Conclusions: The density and location of the target object can affect the number of reproducible radiomic features, and its density can also affect the number of location-stable radiomic features. The problem of feature reliability should be treated cautiously in radiomic research on CBCT.

Keywords: Cone-Beam Computed Tomography; Location-stability; Phantom Study; Radiomic Features; Reproducibility.

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Figures

Figure 1.
Figure 1.
Before statistical analysis of the data, we need to finish (a) Solution preparation and loading, (b) Phantom assembling and grouping, (c) Phantom shooting and image acquisition and (d) Features extraction in turn.
Figure 2.
Figure 2.
69 features’ CCC values distribution is shown in this heatmap, with 20 features reproducible in at least one series. The naming rule for the y axis is ”concentration.group”. Abbreviations are explained as follows: GLCM: grey level co-occurrence matrix; GLRLM: grey level run length matrix; NGTDM: neighborhood grey tone difference matrix; GLSZM: grey level size zone matrix.
Figure 3.
Figure 3.
The distribution of CCC* among each group in six concentration sets.
See this image and copyright information in PMC

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