ESTRO 2020 Abstract Book

S830 ESTRO 2020

compared to the reference predictive models. Their use is recommended in future studies in presence of scarcely represented data, such as rare side effect and high grade toxicities. PO-1536 RadiomiK: a phantom to test repeatability and reproducibility of CT-derived Radiomic Features S. Pallotta 1 , D. Cusumano 2 , A. Taddeucci 3 , M. Benelli 4 , R. Sulejmeni 1 , J. Lenkowicz 2 , S. Calusi 1 , L. Marrazzo 5 , C. Talamonti 1 , G. Belli 3 , M. De Spirito 2 , A. Barucci 6 , N. Zoppetti 6 1 Azienda Ospedaliero Universitaria Careggi - University of Florence, Department of Biomedical Experimental and Clinical Sciences "Mario Serio", Florence, Italy ; 2 Fondazione Policlinico Universitario A. Gemelli IRCCS, Medical Physics, Rome, Italy ; 3 AOU-Careggi, Health Physics, Florence, Italy ; 4 Hospital of Prato, Bioinformatics Unit, Prato, Italy ; 5 Azienda Ospedaliero Universitaria Careggi, Medical Physics Unit, Florence, Italy ; 6 CNR Florence Research Area-, Institute of Applied Physics "Nello Carrara", Florence, Italy Purpose or Objective Radiomics has been demonstrated to have a role in several clinical processes. Although radiomic approach is interesting it suffers from several noise sources, associated with image acquisition and post-processing (1). Understanding noise sources due to image acquisition permits to guide the creation of local prospective imaging protocols. Aim of this work is to present a phantom developed to test reproducibility and repeatability of radiomic features extracted from CT images. Material and Methods The phantom - RadiomiK (fig. 1a) comprises 23 elements imbedded in a layer of epoxy resin. The shape, materials and filling textures were chosen to produce a wide range of radiomics feature values capable to mimic those found in CT images of human being. 15 elements with cubic, cylindrical or conical shape, (1cm side, diameter and high) were fabricated with a 3D printer using PLA, FLEX and PETG. Honeycomb and gyroid textures, with air-filled holes and different filling percentage were employed. 3 cubes and 2 cylinders were made using slabs of Solid Water, Cortical Bone and Lung (Gammex-RMI, Middleton, WI, USA). Finally, 3 elements made with 25 mini-cubes (2mm side) were assembled creating three different patterns.

Tests- 9 CT studies were acquired with a Somatom Definition Flash (Siemens, Erlangen, Germany) in helical mode, using the SAFIRE iterative reconstruction algorithm with strength 3, kernel i50f, 2mm slice thickness, 0,25mm pixel size, 120kV and 300, 250, 200, 175, 150, 125, 100, 75, 50 mAs. MIM-Maestro (MIM Software, Cleveland, OH) was used to manually segment 4 ROIs and the Moddicom package (2) to extract Radiomic features. Results A CT cross section of Radiomik (fig 1b) shows the different materials, textures and fill levels (20%, 60%, 100%) of the 23 elements; their mean HU values ranged between -630 HU to 1420HU and 90 HU for epoxy resin. Contrast between inserts and background is good but the boundary of inserts is not sufficiently sharp to enable automatic segmentation with threshold or region growing algorithms. The 4 ROIs considered for Radiomic Features extraction are highlighted in fig 1b). For each radiomic feature (n=74), we calculated the Spearman’s correlation coefficient between feature value and acquisition mAs (fig.2). Radiomic features showing the highest dependency from mAs were mostly not shared across the different ROIs, suggesting a not-negligible interplay between ROI characteristics and CT protocol setting in feature value estimation.