Abstract
.Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting.Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes.Results: The structural similarity index between the segment used from the patients’ DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with .Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.
Highlights
In addition to commonplace scan modes, such as different tube potentials and currents, we evaluated quantitative radiomics (QR) feature robustness with adaptive statistical iterative reconstruction (ASiR), and the phantom positioned vertically off-center by 30 mm in the inferior
We evaluate its impact on QR features in this study because the off-center placement of the patient within the computed tomography (CT) gantry misplaces the thickest portion of the bow-tie filter relative to the patient’s anatomy, which leads to increased beam hardening artifacts and, increased noise or variability of CT Hounsfield unit (HU) values.[39]
The strategy proposed here is to derive regions of interest (ROIs) tumor and normal tissue features from human CT scans and to custom print a CT phantom capturing a facsimile of those features
Summary
There is a growing body of literature about the role of quantitative radiomics (QR) metrics as cancer imaging biomarkers for predicting lesion malignancy and the efficacy of treatments.[1,2,3] promising, a general lack of standardization and inconsistent performance of QR metrics across different computed tomography (CT) scan modes is well established.[4,5,6,7,8,9,10] A solution to improve standardization and quality control (QC) of the QR pipeline should include phantoms that can filter unreliable metrics.[11,12,13]. CT equipment operators have used QC phantoms to monitor the imaging performance of clinical scanners.[14] CT QC phantoms are engineered with homogeneous materials that lack the texture or shapes of tumors seen on CT exams. As CT scanner hardware and software become more technologically sophisticated, the phantom components will need to take on more realistic properties
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