ESTRO 2020 Abstract book

S263 ESTRO 2020

position, serving as ground-truth reference. Based on the evaluation of planning CT (pCT) and cCT data on the level of CT images, dose distributions and derived line-dose profiles, anatomical changes were identified and scored concerning cause and magnitude. The detectability of these changes with PGI was determined by manually comparing expected range shifts from line-dose profiles (pCT vs. cCT) with PGI-derived spot-wise range shifts for distal PBS spots (Fig.1). This evaluation was performed for both, measured as well as simulated PGI data based on cCT (no statistical uncertainty). Furthermore, the sensitivity for a binary differentiation between relevant (strong/moderate) and no relevant anatomical changes within a fraction was determined. Working towards an automated classification of treatment deviations for real- time treatment verification, a simple two-parametric model was established to classify each monitored field into global, local and not clinically relevant anatomical changes.

Conclusion Significant correlations between the occurrence of late side effects and DVH parameters of associated OARs were observed and externally validated for patients with brain tumours receiving PBT. Similar DVH parameters were associated to late alopecia as described for early alopecia following PBT [1]. The relation between median cochlear dose and persistent hearing loss is in agreement with several studies on photon therapy patients. In the future, these NTCP models in combination with models on neurocognition may be used to identify patients who are likely to benefit most from PBT. [1] Dutz A et al. (2019) Radiother Oncol 130, 164-171. OC-0443 First systematic clinical study on detection of anatomical changes in PT using prompt-gamma imaging J. Berthold 1,2 , A. Jost 1,3 , C. Khamfongkhruea 1 , J. Petzoldt 4,5 , J. Thiele 6 , T. Hölscher 6 , P. Wohlfahrt 1,2,7 , G. Pausch 1,2 , G. Janssens 4 , J. Smeets 4 , C. Richter 1,2,6,8 1 OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden- Helmholtz-Zentrum Dresden - Rossendorf- Dresden- Germany, Dresden, Germany ; 2 Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay- Dresden- Germany, Dresden, Germany ; 3 Beuth Hochschule für Technik, Berlin, Germany ; 4 Ion Beam Applications SA, Louvain-la-Neuve, Belgium ; 5 Now with Thermo Fisher Scientific Bremen GmbH, Bremen, Germany ; 6 Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus- Technische Universität Dresden, Dresden, Germany ; 7 Now with Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, USA ; 8 German Cancer Consortium DKTK- partner site Dresden, Germany and German Cancer Research Center DKFZ, Heidelberg, Germany Purpose or Objective Anatomical changes during the course of proton therapy treatment can result in relevant changes in proton range, potentially causing severe under- or overdosage. Verifying the proton treatment, ideally in real-time, is thus highly desirable. Here, we present the first systematic evaluation of the sensitivity of a prompt-gamma-imaging (PGI) based range verification system to detect anatomical changes in prostate-cancer treatments. Material and Methods A PGI slit-camera system was clinically applied to monitor spot-wise proton range deviations during 7 and 9 fractions of hypo-fractionated pencil beam scanning (PBS) treatment for 2 prostate-cancer patients, respectively (2 opposing fields, 1.5 GyE each). For all monitored fractions, in-room control CT scans (cCT) were acquired in treatment

Results From 64 detected anatomical changes in 32 monitored treatment fields, in total 66% (84%) were also identified by measured (simulated) PGI data (Fig.2a). All strong changes (14/64) were identified correctly. For the differentiation between relevant from non-relevant changes, a sensitivity of 69% (95%) was achieved for measured (simulated) PGI data. The first attempt for automated classification was able to reliably differentiate global from local changes (Fig.2b). However, it was more difficult to distinguish treatments with no relevant from local anatomical changes. Also in the ground-truth classification, this decision was sometimes also borderline.