Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

Abstract

Purpose: This study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans. Methods:Using a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values.The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.Results: Twenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, 3/10 3 first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDV's (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDV's (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDV's (%) within ± 10%. Conclusion:We demonstrated the feasibility with which patient informed 3D printe...

Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering

“…A multimaterial 3D printer (PolyJet Objet 260 Connex 3, Stratasys, Eden Prairie, Minnesota) with Voxel Print software was used for the deposition of droplets of ultraviolet-curable photopolymer resins in a layer-by-layer manner. 12,13 In this study, two base photopolymer resins were employed including TangoPlus (material A) and VeroWhite (material B), which range in attenuation of ∼65 Hounsfield unit (HU) to 125 HU at 120 kVp. The resolution of the printer is on the order of 48 × 84 × 30 μm.…”