Assessing the reproducibility of CBCT-derived radiomics features using a novel three-dimensional printed phantom.

Purpose Radiomics modelling is an exciting avenue for enhancing clinical decision-making and personalized treatment. Radiation oncology patients often undergo routine imaging for position verification, particularly using LINAC-mounted cone-beam computed tomography (CBCT). The wealth of imaging data collected in modern radiation therapy presents an ideal use case for radiomics modelling. Despite this, texture feature (TF) calculation can be limited by concerns over feature stability and reproducibility; in theory this issue is compounded by the relatively poor image quality of CBCT, as well as variation of acquisition and reconstruction parameters. Methods In this study, we developed and validated a novel 3D-printed phantom for evaluating CBCT-based texture feature reliability. The phantom has a cylindrical shape (22 cm diameter and 25.5 cm height) with five inner inserts designed to hold custom-printed rods (1 cm diameter and 10-20 cm height) of various materials, infill shapes, and densities. TF reproducibility was evaluated across and within three LINACs from a single vendor using sets of three consecutive CBCT taken with the head, thorax, and pelvis clinical imaging protocols. PyRadiomics was used to extract a standard set of TFs from regions of interest centered on each rod. Two way mixed effects absolute agreement intra-class correlation coefficient (ICC) was used to evaluate TF reproducibility, with features showing ICC values above 0.9 considered robust if their Bonferroni-corrected p-value was below 0.05. Results A total of 63, 87, and 83 features exhibited test-retest reliability for the head, thorax, and pelvis imaging protocols respectively. When assessing stability between discreet imaging sessions on the same LINAC these numbers were reduced to 5, 63, and 70 features respectively. The thorax and pelvis protocols maintained a rich candidate feature space in inter-LINAC analysis with 61 and 65 features, respectively, exceeding the ICC criteria. Crucially, no features were deemed reproducible when compared between protocols. Conclusions We have developed a 3D-phantom for consistent evaluation of texture feature stability and reproducibility. For LINACs from a single vendor, our study found a substantial number of features available for robust radiomics modelling from CBCT imaging. However, some features showed variations across LINACs. Studies involving CBCT-based radiomics must pre-select features prior to their use in clinical-based models.