ESTRO 36 Abstract Book

S442 ESTRO 36 _______________________________________________________________________________________________

influence matrix). After solving the optimization problems, the optimal fluence map is imported back to Eclipse for leaf sequencing and final dose calculation using the Eclipse API. The entire workflow is automated, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the HCO Eclipse API plugin. Optimization requires ~1-3 hours, after which the automated plan including final dose calculation is ready in Eclipse. Results HCO IMRT automatic planning was tested for 10 patients with spinal lesions who had previously been treated to 24 Gy in a single fraction using either VMAT (8 patients) or multi-field IMRT (2 patients). All automated HCO plans used multi-field IMRT. A typical automated and clinical plan comparison is shown in Figure 1, demonstrating improved PTV coverage, cord and esophagus sparing with the automated plan. As shown in Table 1, on average, the automated plan improved PTV coverage (V95%) by 1%, cord maximum dose by 2%, cord D0.35cc by 12%, cauda maximum dose by 15%, and esophagus V18Gy by 100%. All HCO plans met all clinical planning criteria. Table-1. Comparison of clinical and HCO automated plans for ten patients. For each criterion, the better score is bolded.

Conclusion RapidPlan model evolves when the sub-optimal constituent training sets were replaced by the improved plans that were re-optimized by the model. One iteration is most cost-effective. PO-0823 Hierarchical constrained optimization for automated SBRT paraspinal IMRT planning M. Zarepisheh 1 , L. Hong 1 , J.G. Mechalakos 1 , M.A. Hunt 1 , G.S. Mageras 1 , J.O. Deasy 1 1 Memorial Sloan Kettering, Medical Physics, New York, United States Minor Outlying Island Purpose or Objective To develop a fully automated approach to IMRT treatment planning using hierarchical constrained optimization and the Eclipse API for SBRT paraspinal cases. Material and Methods This study formulates the IMRT treatment planning problem as a hierarchical constrained optimization (HCO) problem (also known as prioritized optimization). HCO prioritizes the clinical goals and optimizes them in ordered steps. In this study, we maximize tumor coverage first and then minimize critical organ doses in the subsequent steps based on their clinical priorities (e.g., (1) maximize tumor coverage, (2) minimize cord or cauda dose, (3) minimize esophagus dose,...). For each organ we define an objective function, based on the gEUD concept, which correlates with the clinical criterion. At each step, we preserve the results obtained in the prior steps by treating them as hard constraints with a slight relaxation or ”slip” to provide space for subsequent improvement. Maximum dose criteria to the tumor and other organs is always respected through hard constraints applied at all steps. To solve the resultant large-scale constrained optimization problems, we use two commerical solvers, knitro and ampl . The Eclipse API is used to pull patient data needed for optimization (e.g., beam geometries,

Figure-1. Comparison of the clinical and automated plans for a patient. A1-A3 represent the automated plan and C1- C3 represent the clinical plan. Conclusion Hierarchical constrained optimization shows promise as a powerful tool to automate IMRT treatment planning. The automated treatment plan meets all clinical criteria and compares favorably in relevant metrics to the plan generated by planners. Using Eclipse API, we developed a plugin which fully automates the workflow and can be implemented into clinical use after thorough testing.

Poster: Physics track: Treatment planning: applications

PO-0824 IMRT dose painting for prostate cancer using PSMA-PET/CT: a planning study based on histology K. Koubar 1,2 , C. Zamboglou 2,3 , I. Sachpazidis 1,2 , R. Wiehle 1,2 , S. Kirste 2,3 , V. Drendel 2,4 , M. Mix 2,5 , F. Schiller 2,5 , P. Mavroidis 6,7 , P.T. Meyer 2,5 , A.L. Grosu 2,3 , D. Baltas 1,2 1 Medical Center University of Freiburg - Faculty of Medicine - University of Freiburg, Division of Medical Physics - Department of Radiation Oncology, Freiburg,