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Assessing the reproducibility of CBCT‐derived radiomics features using a novel three‐dimensional printed phantom
Author(s) -
Spuhler Karl D.,
Teruel Jose R.,
Galavis Paulina E.
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.15043
Subject(s) - imaging phantom , reproducibility , cone beam computed tomography , nuclear medicine , medical imaging , quality assurance , dosimetry , feature (linguistics) , medicine , medical physics , biomedical engineering , computer science , radiology , mathematics , computed tomography , statistics , external quality assessment , pathology , linguistics , philosophy
Purpose Radiomics modeling 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 modeling. 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 three‐dimensional (3D) printed phantom for evaluating CBCT‐based TF 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 TF stability and reproducibility. For LINACs from a single vendor, our study found a substantial number of features available for robust radiomics modeling from CBCT imaging. However, some features showed variations across LINACs. Studies involving CBCT‐based radiomics must preselect features prior to their use in clinical‐based models.