ESTRO 36 Abstract Book

S76 ESTRO 36 2017 _______________________________________________________________________________________________

Conclusion The systematic sensitivity study revealed the capability of the PGI slit camera to detect range shifts under clinical conditions. In both treatment modalities, global range shifts can be detected. Additionally, in PBS a spot-wise comparison allows also the determination of interfractional local range shifts. Moreover, a still ongoing evaluation of PBS measured and simulated spot-wise profiles for absolute range verification will be presented. OC-0154 Proton therapy patient selection for oropharyngeal cancer patients: the impact of treatment accuracy M. Hoogeman 1 , S. Breedveld 1 , M. De Jong 2 , E. Astreinidou 2 , L. Tans 1 , F. Keskin-Cambay 1 , R. Bijman 1 , S. Krol 2 , S. Van de Water 1 , T. Arts 1 1 Erasmus MC Cancer Institute, Radiation Oncology, Rotterdam, The Netherlands 2 Leids University Medical Center, Radiation Oncology, Leiden, The Netherlands Purpose or Objective Comparative treatment planning including Normal Tissue Complication Probability (NTCP) evaluation has been proposed to select patients for proton therapy. NTCP, however, does not only depend on the type of radiation used, but also on the size of the safety margins or degree of robustness needed to account for treatment-related uncertainties. In this study, for the first time to our knowledge, the impact of margins and robustness settings to the selection of oropharyngeal cancer patients is investigated using fully automated comparative treatment planning. Material and Methods CT and contour data of 78 consecutive oropharyngeal patients were imported in our in-house developed system for automated treatment planning for Intensity-Modulated photon (IMRT) and proton radiotherapy (IMPT). Treatment plans were generated fully automatically for a simultaneously integrated boost scheme prescribing 70 Gy RBE to the primary tumor and pathological lymph nodes and 54.25 Gy RBE to the elective nodal areas in 35 fractions. IMRT treatment plans were generated with a 0, 3, or 5mm margin. IMPT 'minimax” robust optimized treatment plans were generated for five different setup and range robustness settings. Five validated NTCP models (see Fig. 1) proposed for IMPT patient selection were used in this study. Following Dutch consensus guidelines, patients were selected for IMPT if IMPT reduced NTCP by 10% or 5% for a grade II or a grade III complication, respectively. Results In total 624 treatment plans were generated automatically and approved by the authors. Figure 1 shows that the percentage of patients selected for IMPT decreases with increasing robustness setting for a given margin and also decreases with decreasing margin for a given robustness setting. In contrast to the size of the margin, the degree of robustness has little impact on patient selection for tube feeding dependence, which is the only grade III complication. For the other complications the impact of the degree of robustness setting is noticeably higher. For patient-rated sticky saliva, nearly no patient is selected for IMPT if robustness is included. If we consider high- precision IMRT using a 3mm margin and high-precision IMPT using a robustness setting of 3mm for setup and 3% for range errors, most patients are selected for proton therapy based on problems swallowing solid food (51.3%), followed by tube feeding dependence (37.2%) and decreased parotid flow (29.5%). Patient-rated sticky saliva and patient-rated xerostomia contributed only with 1.3% and 7.7% respectively.

Conclusion This study shows that treatment accuracy cannot be ignored in estimating the number of patients that will be selected for proton therapy based on comparative treatment planning and NTCP evaluation. We also conclude that IMRT as well as IMPT should be optimized for accuracy to ensure a sustainable use of proton therapy.

Proffered Papers: Imaging and image analysis

OC-0155 Automated lung tumour delineation in cine MR images for image guided radiotherapy with an MR- Linac B. Eiben 1 , M.F. Fast 2 , M.J. Menten 2 , K. Bromma 2 , A. Wetscherek 2 , D.J. Hawkes 1 , J.R. McClelland 1 , U. Oelfke 2 1 University College London, Centre for Medical Image Computing, London, United Kingdom 2 The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Joint Department of Physics, London, United Kingdom Purpose or Objective Respiratory-induced lung tumour movement is a significant challenge for precise dose delivery during radiotherapy. MR-Linac technology has the potential to monitor tumour motion and deformation using continuously acquired 2D cine MR images. In order to target tumours in their current shape and position the tumour outline must be established automatically. In this study we compared four automatic contouring algorithms that delineate the tumour in sequential cine MR images based on manually contoured training images. Material and Methods Five 1 min 2D cine MR images (Fig. 1) were acquired for two patients. Each sequence was split into a training set of ten source images and a test set of about 100 images. Method (1) is a multi-template matching, with a template taken from each source image centred on the tumour. For every test image the best position of each template is evaluated and the most similar match is selected. Method (2) uses a pulse-coupled neural network (PCNN) to improve the grey-value contrast between tumour and healthy tissue thus aiding the auto-contouring. The PCNN and associated erosion and dilation parameters were trained on the training sets using an accelerated particle swarm