ESTRO 2022 - Abstract Book

S546

Abstract book

ESTRO 2022

Conclusion A method to accurately measure WEPL using FP-PR with a reduced number of energies tailored to the patient anatomy has been established in silico and evaluated with respect to in vivo patient MLIC-PR measurements in head and neck cancer patients. Patient specific FP-PRs hold the potential to assist online range verification quality control processes within online adaptive proton therapy workflows.

OC-0620 Prompt-gamma imaging for prostate cancer proton therapy: CNN-based detection of anatomical changes

J. Pietsch 1,2 , N. Piplack 1,3 , J. Berthold 1,2 , C. Khamfongkhruea 1,4 , J. Thiele 5 , T. Hölscher 5 , E. Traneus 6 , . Janssens 7 , J. Smeets 7 , K. Stützer 1,2 , S. Löck 1,5,8 , C. Richter 1,2,5,8 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; 2 Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany; 3 Faculty of Electrical and Computer Engineering, Technische Universität Dresden, Dresden, Germany; 4 Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bankok, Thailand; 5 Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; 6 RaySearch Laboratories AB, Research, Stockholm, Sweden; 7 Ion Beam Applications SA, Research, Louvain-la-Neuve, Belgium; 8 German Cancer Consortium (DKTK), partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany Purpose or Objective A clinical study (PRIMA) regarding the potential of range verification in proton therapy by prompt-gamma imaging (PGI) is carried out at our institution. As a step towards the automatic evaluation of the measured PGI data, we present an approach to detect anatomical changes in prostate cancer patients from realistically simulated PGI data using convolutional neural networks (CNNs). Materials and Methods In-room control CTs (cCTs) were acquired in treatment position before monitoring 142 field deliveries of 10 hypo-fractioned (3Gy/fraction) prostate cancer patients with a PGI slit camera (range: 8-18 fields/patient). After manual CT registration and dose recalculation, spot-wise shifts of integrated depth-dose (IDD) profiles between cCTs and planning CTs were extracted at the 80% distal falloff position and used for ground-truth classification. Treatment fields were considered to be affected by relevant anatomical changes of the patient if >0.1% of all spots (with at least 0.1% of the total monitor units per field) had absolute IDD shifts above 5 mm. These parameters lead to a field-wise IDD ground-truth classification in optimal agreement with a prior manual field-wise classification based on dose difference maps.