ESTRO 2023 - Abstract Book

S1340

Digital Posters

ESTRO 2023

Conclusion Incorporating the preceding gaze angle predictive algorithm into the clinic treatment decision process as an initial suggestion and as a reference gaze direction promises to streamline the clinical workflow as an important step towards automated treatment planning in ocular proton therapy. The following step will be to extend the dosimetric evaluation past the four cases to include cases from the full testing dataset.

PO-1643 Towards automated treatment planning for robotic stereotactic radiosurgery

M. Nachbar 1 , G. Acker 2,3,4 , C. Senger 2 , K. Mauersberger 2 , R. Schild 2 , W. Kuschke 2 , D. Zips 5,6

1 Charité-Universitätsmedizin Berlin , Department of Radiation Oncology and Radiotherapy, Berlin, Germany; 2 Charité Universitätsmedizin Berlin, Department of Radiation Oncology and Radiotherapy, Berlin, Germany; 3 Charité Universitätsmedizin Berlin, Department of Neurosurgery, Berlin, Germany; 4 Berlin Institute of Health, Berlin Institute of Health, Berlin, Germany; 5 Charité-Universitätsmedizin Berlin, Department of Radiation Oncology and Radiotherapy, Berlin, Germany; 6 German Cancer Consortium (DKTK), , Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany Purpose or Objective Automated planning is aiming for robust radiotherapy treatment planning allowing extensive simulations. While solutions are available for inverse optimization of treatment plans by iteratively changing constraints, there is no complete solution for CyberKnife (CK) treatment planning. Whereas, constraint based approaches can be used in a second step, the CK requires a definition of collimators prior to optimization. Therefore, within this work an automated analysis of target structures and identification of best possible collimator combinations for treatment plan optimization was developed. This solution was evaluated within a planning study in comparison with clinically used combinations. Materials and Methods The developed solution was programmed in Python 3.9. A 2D visualization of the planning target volume (PTV) from every 60 degrees in all three dimensions was generated and the coverage of different collimator combinations analyzed. Collimator positions were defined as usable, if within the 2D-Visualisation 90% of the collimator Beams-eye-view (BEV) was covered by the target structure. For each combination and orientation, the number of available, usable collimator positions and the overall structure coverage were calculated. The most effective collimator combination, which covers 98% of the target on over 85% of the 2D BEV with the least number of necessary collimator positions was chosen for further analysis. In a treatment planning study, the clinical treatment (CP) plan of ten meningioma patients was re-optimized with the new