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increasing difference from the original value with reducing interpolated voxel size. Conclusion We have highlighted several texture features that vary depending on the choice of interpolated voxel size. Features sensitive to interpolation should be used with caution when comparing datasets and developing prognostic models using 3D TA metrics. A standardised approach should be adopted and future work aims to quantitatively determine feature variation and robustness. Figure 1: 4 GLCM features; Dissimilarity, Difference Entropy, Correlation and Sum Entropy. Feature values calculated for original and 5 interpolated voxel sizes. Patients ranked for each feature based on the original dimension.

year; subset of them have long-term survival. In fact identifying the features affecting survival to aid improved treatment planning. There are advanced method using Data mining that help to find relation between the features and survival. The goal of research is to identify the GBM patient variables association of GBM using rule association mining. Material and Methods To calculate GBM outcome, 55 Iranian histological proven glioblastoma patients between 2012 and 2014 were included. Radiotherapy or chemotherapy (one or both) would be performed along with the surgery. The follow up of cutoff point was 12 months after diagnosing. 20 patients variables were used to calculate outcome of GBM patients that includes Age, Gender, KPS, Chemotherapy, Radiotherapy, Cyst, Enhancing rim, Margin, Solid ,Multifoculity, Satellites, Tumor cross midline (TCM), Edema cross midline (ECM), Side, Volume of necrosis(neccal), Volume of enhancement(Encal), Volume of non-enhancement (nEncal), Volume of edema(edemcal), major length of tumor , major width of tumor. According to VASARI research project, features were selected and categorized. Automated association rule mining, Apiori algorithm, was run by IBM SPSS Modeler 18. Obtained rules were evaluated by confidence and lift formulas beside conformation of neurosurgical physician. Results 55 glioblastoma patients [male/female: 29/26; median age: 56; alive/dead: 24/31; median survival: 9.12 months] were selected. By association rule mining separated rules were generated for alive and dead class. Redundant obtained rules were manually removed based on domain knowledge. Table 1 lists the 5 the best rules for alive status.

Table 1: Rules related Alive status

References [1] R. J. Gillies et al. “Radiomics: Images Are More than Pictures, They Are Data.,” Radiology , vol. 278, no. 2, p. 151169, 2015. [2] Zwanenburg et al . Multicentre initiative for standardisation of image biomarkers, Radiother. Oncol. Vol 123 Supp 1. [3] M.-C. Desseroit et al. , “Reliability of PET/CT Shape and Heterogeneity Features…” J. Nucl. Med. , vol. 58, no. 3, pp. 406–411, 2017 EP-2118 Identifying focal point in Glioblastoma multiforme using Association rule mining. M. Jajroudi 1 , R. Reiazi 2 , A. Azarhomayoun 3 1 Shiraz university of Medical Sciences, Medical Informatics- School of Management and Medical Information Sciences-, TEHRAN, Iran Islamic Republic of 2 Medical School- Iran university of Medical Sciences, Medical Physics, tehran, Iran Islamic Republic of 3 Tehran University of Medical Sciences, Sina Trauma Research Center- Department of Neurosurgery- Sina Hospital, Tehran, Iran Islamic Republic of Purpose or Objective Glioblastoma multiforme (GBM) is the most aggressive and fatal primary brain tumor. However the prognosis is usually very poor and survival of patients is less than 1

There are 5 the best rules for dead status in table 2. Table 2: Rules related dead status

Conclusion In this research we analyzed GBM Patients data by association rule mining and made rules related alive and dead status. We believe that such analysis can help to identify the factors affecting survival time furthermore aid the physician to make better decision to select proper treatment.