ESTRO 36 Abstract Book

S905 ESTRO 36 2017 _______________________________________________________________________________________________

individual clinical risk factors (PSA >20 ng/ml, or Gleason score of 8–10, or clinical stage T3a) are not significantly associated with distant metastasis (P-value is 0.493, 0.087, 0.109, respectively). In the multivariate forward analysis, imaging characteristics of main tumor side wall invasion by anatomical T2 MRI is the only significant risk factor predicting distant metastasis (odds ratio 42.25, confidence interval 5.1-346.5, P-value <0.001). The PET regional tumor texture features can further divide patients into with or without distant metastasis by using high intensity long run emphasis value > -0.40 and low intensity run emphasis value <0.26 (Figure 1). The Sensitivity and specificity of the multivariate tree model was 80% and 80%, respectively.

of 0.5, 0.6 and 0.8cm were investigated. These ROIs/VOIs were first centred on the maximum activity voxel; a second analysis was made changing the location from the voxel to the region (ROI5voxels) or the volume (VOI7voxels) with the maximum value. Two additional VOIs were defined as 3D isocontours respectively at 70% and 50% of the maximum voxel value. The SUV metrics were normalized by the corresponding 3D static SUV. Converting to recovery coefficients (RC) allowed us to pool data from all institutions, while maintaining focus solely on motion. For each RC from each motion setting we calculated the mean over institutions, we then looked at the standard deviation (Sd) and spread of each averaged RC over each motion setting. Results For the institutions visited we found that RCVOI70% and RCVOI50%, yielded over the 14 metrics the lowest variability to motion with Sd of 0.04 and 0.03 respectively. The RCs based on ROIs/VOIs centered on a single voxel were less impacted by motion (Sd: 0.08) compared to region RCs (Sd: 0.14). The averaged Sd over the RCs based on VOIs and ROIs was 0.12 and 0.11 respectively. Conclusion Quantification over breathing types depends on ROI/VOI definition. Variables based on SUV max thresholds were found the most robust against respiratory noise. EP-1683 Fractals in Radiomics: implementation of new features based on fractal analysis D. Cusumano 1 , N. Dinapoli 2 , R. Gatta 2 , C. Masciocchi 2 , J. Lenkowicz 2 , G. Chilorio 2 , L. Azario 1 , J. Van Soest 3 , A. Dekker 3 , P. Lambin 3 , M. De Spirito 4 , V. Valentini 5 1 Fondazione Policlinico Universitario A.Gemelli, Unità Complessa di Fisica Sanitaria, Roma, Italy 2 Fondazione Policlinico Universitario A.Gemelli, Divisione di Radioterapia Oncologica- Gemelli ART, Roma, Italy 3 Maastricht University Medical Center, Department of Radiation Oncology, Maastricht, The Netherlands 4 Università Cattolica del Sacro Cuore, Istituto di Fisica, Roma, Italy 5 Università Cattolica del Sacro Cuore, Department of Radiotherapy - Gemelli ART, Roma, Italy Purpose or Objective A fractal object is characterized by a repeating pattern that it displays at different size scales: this property, known as self-similarity, is typical of many structures in nature or inside human body (a snow flake and the neural networks are just some examples). The fractal self-similarity can be measured by Fractal Dimension (FD), a parameter able to quantify the geometric complexity of the object under analysis. Aim of this study is to introduce in Radiomics new features based on fractal analysis, in order to obtain new indicators able to detect tumor spatial heterogeneity. These fractal features have been used to develop a predictive model able to calculate the probability of pathological complete response (pCR) after neoadjuvant chemo-radiotherapy for patients affected by locally advanced rectal cancer (LARC). Material and Methods An home-made R software was developed to calculate the FD of the Gross Tumor Volume (GTV) of 173 patients affected by LARC. The software, validated by comparing

Conclusion By providing excellent anatomical, functional, and metabolic information, integrated PET/MR enhances the staging of metastatic disease in high risk Pca. Imaging characteristics including pelvic side wall invasion and tumor metabolic heterogeneity may have crucial role in patient management. EP-1682 Comparison of SUV based on different ROIs and VOIs definitions: a multi-center 4D phantom study M. Lambrecht 1 , K. Ortega Marin 1 , M. La Fontaine 2 , J.J. Sonke 2 , R. Boellaard 3 , M. Verheij 2 , C.W. Hurkmans 1 1 Catharina Ziekenhuis, Physics/Radiotherapy, Eindhoven, The Netherlands 2 Netherlands cancer institute, Radiotherapy, Amsterdam, The Netherlands 3 University medical center- university of Groningen, Nuclear medicine, Groningen, The Netherlands Purpose or Objective In the context of the EORTC LungTech trial, a QA procedure including a PET/CT credentialing has been developed. This procedure will ultimately allow us to pool data from 23 institutions with the overall goal of investigating the impact of tumour motion on quantification. As no standardised procedure exists under respiratory conditions, we investigated the variability of 14 SUV metrics to assess their robustness over respiratory noise. Material and Methods The customized CIRS-008A phantom was scanned at 13 institutions. This phantom consists of a 18 cm long body, a rod attached to a motion actuator, and a sphere of either 1.5 or 2.5cm diameters. Body, rods and spheres were filled with homogeneous 18FDG solutions representative of activity concentrations in mediastinum, lung and tumour for a 70kg patient. Three respiratory patterns with peak-to-peak amplitudes and periods of 15mm/3sec, 15mm/6sec and 25mm/4sec were tested. Prior to scanning in respiratory condition, a 3D static PET/CT was acquired as reference. During motion, images were acquired using 3D or 4D gated PET(average image) according to institutional settings. 14 SUV(mean) metrics were obtained per acquisition varying VOI/ ROI shape and location. Three ROIs and three VOIs with respective radii