ESTRO 36 Abstract Book

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Conclusion Despite higher symptoms’ burden after WBRT that are attributed to the side effects of RT, like appetite loss, drowsiness, and hair loss, QLQ-C30 global health status, physical functioning and future uncertainty favored WBRT in comparison with SRT-TB in our study. This may be related to the compromised brain tumor control with omission of WBRT; however, we should be aware that in brain metastases patients many factors may influence QoL. PO-0627 Prediction of radiosurgery response of brain metastases using convolutional neural networks Y. Cha 1 , M.S. Kim 1 , C.K. Cho 1 , H. Yoo 1 , W.I. Jang 1 , Y.S. Seo 1 , J.K. Kang 1 , E.K. Paik 1 1 Korea Institute of Radiological & Medical Sciences, Department of Radiation Oncology, Seoul, Korea Republic of Purpose or Objective A deep learning concept based on artificial convolutional neural networks (CNN) is regarded as an emerging radiomics methodology because it uses minimal amount of image preprocessing. Metastatic brain tumor is presumed an appropriate model for radiomics study because of round shape and clear boundary of tumor. The purpose of this study is to predict radiation response of metastatic brain tumor receiving stereotactic radiosurgery (SRS) using the radiomics model based on CNN. Material and Methods CNN is a kind of artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of visual cortex. We implemented a CNN system to process CT images using the numerical computation library 'TensorFlow'. The 110 metastatic brain lesions with longest diameter of 1-3.5 cm treated with SRS between 2007 and 2015 were retrospectively evaluated. Through the radiologic review within 3 months after SRS, all lesions ware divided 2 groups: responder (complete or partial response) and non-responder (stable or progression) by Response Evaluation Criteria in Solid Tumor (version 1.1). Responder and non-responder were 57 and 53 lesions, respectively. 110 data-sets which composed of extracted images and matched response classification were randomly assigned to 3 cohorts; training, validation and evaluation cohort. And our CNN system was trained by data-sets of training cohort. Then the system was optimized by adjusting training parameters using data-sets of validation cohort. After sufficient training and optimization, a CNN system reliably predicts classification of the arbitrarily inputted images. We inputted images of evaluation cohort into the trained CNN system. Then the system predicted the response classification of inputted images. The above process was repeatedly performed with changing the number ratio of data-set in each cohort and the assignment of data-sets to cohorts, respectively. Results The range of accuracy of prediction was elevated from 70% to 83% as increasing the number of data-sets of training cohort from 60 to 80. On 80 training data-sets, average 73% sensitivity and 83% specificity in predicting non- responder were achieved. Conclusion CNN based metastatic brain tumor CT image training and classification system was successfully implemented. The prediction of early response after SRS to metastatic brain tumor using the system was achieved effectively. To improve the performance of CNN based prediction system, the number of training data should be increased. This first study of prediction of radiosensitivity using CNN provides initial evidence of potential applicability of CNN based radiomics method to clinical radiation oncology field.

PO-0628 Correlation between 18F-FDOPA uptake and tumor relapse in recurrent high-grade gliomas I. Chabert 1,2,3,4 , F. Dhermain 5 , S. Bibard 1 , S. Reuze 1,3,4 , A. Schernberg 5 , F. Orlhac 4,6 , I. Buvat 6 , E. Deutsch 3,4,5 , C. Robert 1,3,4 1 Gustave Roussy, Radiotherapy - Physics, Villejuif, France 2 Institut Curie, Centre René Huguenin, St-Cloud, France 3 Univ. Paris-Sud, Université Paris-Saclay, Le Kremlin- Bicêtre, France 4 INSERM, U1030, Villejuif, France 5 Gustave Roussy, Radiotherapy, Villejuif, France 6 IMIV, CEA- Inserm- CNRS- Univ. Paris-Sud- Université Paris-Saclay- CEA-SHFJ, Orsay, France Purpose or Objective Patients suffering from high-grade gliomas have a median survival time of 14 months despite various treatment strategies. Our purpose was to investigate whether 18 F- FDOPA PET imaging could predict tumor relapse areas and improve tumor delineation in recurrent high-grade gliomas treated by radio-chemotherapy. Material and Methods This prospective study started in 2015 included 8 patients suffering from recurrent high grade gliomas (grade 4) who received radiotherapy [from 40 Gy to 50 Gy in 2.5 or 4 Gy/fraction] associated with Bevacizumab chemotherapy. Subjects underwent pre-treatment CT, T1-Gd, T2 FLAIR acquisitions and a 18 F-FDOPA scan. All images were registered to the planning CT using a rigid algorithm. One senior radiotherapist delineated Gross Tumor Volumes (GTV) on anatomical MR images. A large region of interest was manually drawn around the first recurrence site on the 18 F-FDOPA images and two thresholds t of 30% and 40% of the maximum standardized uptake value (SUV max ) were applied to deduce the regions of high 18 F-FDOPA uptake (V PET,t ). Follow-up anatomical MR images were used to localize second relapse areas (GTV’). Correlations between all volumes were analyzed using five indexes. I 1,t measures the percentage of V PET,t not included in the anatomically-defined GTV. I 2 and I 3,t respectively measure the percentage of GTV’ included in GTV and V PET,t . I 4,t measures the percentage of V PET,t included in GTV’. I 5,t measures the percentage of V PET,t not included in the GTV which was predictive of relapse. This index is meaningful only if GTV’ and GTV are different.

Results Indexes obtained for each patient are presented in Table 1. Six patients for whom relapse was confirmed anatomically were included in the analysis. For 5 patients, I 2 was lower than 40%, indicating a large progression of the tumor outside the GTV. For 4 patients, I 1,30% and I 1,40% values were between 69-82 % and 44-68 %, showing that additional information was provided by 18 F-FDOPA images. I 3 values rapidly decreased when the threshold t increased (30 % to 40%). For t = 30%, values were greater than 50 % for 3 patients. For these patients, I 4,30% values were between 34