Impact of slice thickness on reproducibility of CT radiomic features of lung tumors

Abstract

Background: Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology. There is scarcity of research that how ST affects variability of RF in LC. The present study helps in identifying the specific RF categories affected by variations in ST and provides valuable insights for researchers and clinicians working with RF in the field of LC.Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors.

Methods: This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm STwas reconstructed retrospectively. RF were extracted from the CECT thorax images of both ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin's Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses.

Results: Out of 107 RF, 66 (61.6%) exhibited a statistically significant distinction (p<0.05) when comparing two ST and while 41 (38.3%) RF did not show significant distinction (p>0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility.

Conclusions: The ST had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.

Keywords: CT Parameters; Computed Tomography; Lung Cancer; Radiomics; Slice Thickness.

Figure 1.. An axial CT image of a 52-year-old male with adenocarcinoma showing manual segmentation of tumour using 3D Slicer at (a) 2mm and (b) 5mm slice thickness.

Figure 2.. Number of significant (p<0.05) and non-significant (p>0.05) features in each RF category between 2-mm and 5-mm slice thickness.

Figure 3.. Mean concordance correlation coefficient (CCC) of each radiomic feature category between 2-mm and 5-mm slice thickness.