The Discriminative Power and Stability of Radiomics Features With Computed Tomography Variations

Abstract

Objectives: The aims of this study were to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data. Materials and Methods: A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. The stability and discriminative power of 86 radiomics features were assessed in measurements taken from 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing. Pairwise parameter group and pairwise tissue class comparisons were performed using Wilcoxon signed rank tests. Results: In total, 19,264 feature stability tests and 8256 discriminative power tests were performed. The 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. The 86 radiomics features were ranked according to the cumulative sum of successful stability and discriminative power tests. Conclusions: The differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.

Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study

“…Previous studies sought to identify radiomics features that were stable across influences on image quality resulting from the use of different scanner systems and acquisition and reconstruction methods [9,12]. Here, we sought to identify image quality that improves the yield of stable and reliable radiomics features.…”

“…A polyethylene film (8 g/m 2 ) served as thermoplastic adhesive, replacing the toner used in previous work [15]. The technique enables the manufacture of realistic textured phantoms and was used previously to create phantoms for the analysis of radiomics features [12,13]. The phantom used here consisted of repeated prints of the same template image, which means that all acquired phantom images displayed the same abdominal slice of the patient.…”

“…Given these limitations, the present work used advances in 3D printing of realistic textured radiomics phantoms [12,13] to create a phantom simulating a patient with hepatic metastases. The phantom was used to independently evaluate four generations of reconstruction algorithms for fundamental differences in image quality for radiomics.…”

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

“…Many studies [1, [28][29][30][31] have also explored the discriminative power of radiomic features. However, the reproducibility of a radiomic feature does not guarantee its discriminative power [32,33], and thus the two aspects of reproducibility and discriminative power cannot be treated in isolation. For instance, a feature may have excellent reproducibility across scanner and protocol variations but have no discriminative power for the problem of interest.…”

Assessing radiomics feature stability with simulated CT acquisitions

“…Especially for CT, advances in 3D printing technologies allow printing volumetric patient images using materials with attenuation properties comparable to human tissue. Recent work reported variability studies using such phantoms 23 – 25 .…”