ESTRO 36 Abstract Book

S912 ESTRO 36 2017 _______________________________________________________________________________________________

dose rate image-guided uterovaginal brachytherapy of 15 Gy. The primary tumor was delineated on the PET images using a fixed threshold (40% of SUV max ) within a manually drawn volume of interest (VOI), called VOI-T. A 73 mL sphere was drawn in the healthy liver considered as homogeneous (VOI-L). For each VOI, 5 conventional indices (SUV mean , SUV max , SUV peak in a 1 mL sphere, metabolic volume, tumor lesion glycolysis) and 6 3D textural indices were calculated after resampling the VOI SUV between 0 and 40 using 128 gray levels: Homogeneity and Entropy from the Gray-Level Co-occurrence Matrix, Short-Run Emphasis (SRE), Long- Run Emphasis (LRE), Low Gray-level Zone Emphasis (LGZE) and High Gray-level Zone Emphasis (HGZE). Wilcoxon’s tests were performed between G1 and G2 in VOI-L to determine to what extent technological differences and image properties influence radiomic feature values. A stepwise model selection using the Akaike Information Criterion was applied to determine the best 4-feature signature for local recurrence prediction in both groups, used successively for training and validation. Delong’s tests between AUC were performed to evaluate if the signature was more powerful than SUV max only.

AUC=0.76 and was significantly more powerful than SUV max according to Delong’s test (p=0.02). G1 signature was not validated in G2, yielding to an AUC less than that obtained with SUV max only.

Conclusion Some conventional and textural features are strongly dependent on the PET device and acquisition parameters such as voxel size. A robustness analysis should be performed before each multi-centric radiomic study, to evaluate the possibility of gathering data from different devices. Multivariate analysis showed that radiomic features can predict LACC local recurrence with a better accuracy than SUV max for recent PET devices. The creation of an external validation cohort is in progress to confirm the results. EP-1693 Functional MRI to individualize PTV margins to seminal vesicles with suspected cancer involvement S. Damkjaer 1 , J. Thomsen 1 , S. Petersen 1 , J. Bangsgaard 1 , M. Aznar 2 , I. Vogelius 1 , P. Petersen 1 1 Rigshospitalet, Department of Oncology- Section for Radiotherapy - 3994, København, Denmark 2 University of Oxford, Clinical Trial Service Unit- Richard Doll Building, Oxford, United Kingdom Purpose or Objective For external beam radiotherapy of prostate cancer patients, the information from pre-treatment MRIs can give patient specific and visual evaluation of suspected pathologically involved volumes in the seminal vesicles (SV) as an important addition to probability based nomograms [1]. We investigate the impact of

Results SUV max

, SUV peak , Homogeneity and SRE computed in VOI-L were significantly different between the two devices (p<0.05). These p-values suggested that data coming from the two PET devices can therefore not be gathered. In G1, the best 4-feature signature was a combination of Entropy, SUV mean , SUV max and SRE (AUC=0.77) and in G2, a combination of SUV peak , Homogeneity, LGZE, HGZE (AUC=0.86). G2 signature was validated in G1 with