ESTRO 36 Abstract Book

S923 ESTRO 36 _______________________________________________________________________________________________

features were not reproducible when using a fixed bin size. Dissimilarity (CR % : 6.3-24.9), homogeneity 1 (CR % : 16.9-22.5), and inertia (CR % : 10.2-22.5) were robust to binning method, scanner type, and SUV activity. Coarseness, contrast, busyness, energy, correlation were not robust (CR % > 30%).

Fig 2. ADC in prostate and dose painted prostate for C im scenario. Conclusion Gleason driven dose painting for prostate cancer using ADC-MRI is feasible to reduce the average dose. The reduction in dose is strongly dependent on the minimum dose assigned to voxels with G <6. EP-1690 Validating the robustness of PET features in a phantom in a multicenter setting T. Konert 1 , M. La Fontaine 2 , S. Van Kranen 2 , W. Vogel 1 , J. Van de Kamer 2 , J.J. Sonke 2 1 Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Nuclear Medicine, Amsterdam, The Netherlands 2 Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Radiation Oncology, Amsterdam, The Netherlands Purpose or Objective PET features may have prognostic or predictive value and could therefore assist treatment decisions. However, PET features are sensitive to differences in data collection, reconstruction settings, and image analysis. It is insufficiently known which features are least affected by these differences, especially in a multicenter setting. Therefore, this study investigates the robustness of PET features in a phantom after repeated measurements (repeatability), due to varying scanner type (reproducibility) and their dependence on binning method and SUV activity. Material and Methods PET scans from a NEMA image quality phantom were used for assessment of PET feature robustness. Scans were acquired on a Philips, a Siemens and a GE scanner from three medical centers (see figure 1 and table 1 for more details). Per sphere, a VOI was created by applying a threshold of 40% of the SUV max . Per VOI, 10 first order statistics and 10 textural features, often reported in literature, were extracted. Two common implementations of image pre-processing, before feature extraction, were compared: using a fixed bin size (SUV = 1) versus a number of fixed bins (64 bins). To examine the feature repeatability, measurements were repeated two or three times on the same scanner. The reproducibility was assessed in images by comparing all scanners. The degree of variation was calculated per VOI with the coefficient of repeatability (1.96 x SD/mean), normalized to a percentage (CR % ). Features were seen as robust with a CR < 30%, matching the level of uncertainty found in response of PERCIST criteria. Wilcoxon signed rank tests were used to estimate the significance of differences due to binning method and p-values ≤ 0.05 were considered significant. Results For an overview of the results, see Table 1. The CR % of SUV max in all scans depended on sphere volume, and ranged from 1.1% (largest sphere) to 15.2% (smallest sphere). In the repeatability study, 9 out of 10 PET features were robust with 64 bins in more than one scanner, and significantly higher (p < 0.05) when compared to using a fixed bin size, where 7 out of 10 PET features were robust. Reproducibility was achieved in 3 out of 10 PET features when 64 bins were used. PET

Conclusion This study indicates that not all PET features are robust in a multicenter setting. Care has to be taken in feature selection and binning method, especially if harmonization of methods across centers is not accomplished. Dissimilarity, homogeneity 1, and inertia seem robust and promising PET features for use in a multicenter setting. Use of fixed bin size should be avoided. EP-1691 Multi-modal voxel-based correlation between DCE-CT/MRI and DWI in metastatic brain cancer C. Coolens 1,2,3 , W. Foltz 1,4 , N. Sinno 1 , C. Wang 1 , B. Driscoll 1 , C. Chung 2,5 1 Princess Margaret Cancer Centre and University Health Network, Radiation Medicine Program, Toronto, Canada 2 University Health Network, TECHNA Institute, Toronto, Canada 3 University of Toronto, Radiation Oncology and IBBME, Toronto, Canada 4 University of Toronto, Radiation Oncology, Toronto, Canada 5 MD Anderson Cancer Center, Radiation Oncology, Houston, USA Purpose or Objective Quantitative model-based measures of dynamic contrast enhanced (DCE) and Diffusion Weighted (DW) MRI parameters have shown variable findings to-date that may reflect variability in the MR acquisition and analysis. This work investigates the use of a voxel-based, multi-modality GPU architecture to include various complimentary solute transport processes into a common framework and