ESTRO 38 Abstract book

S982 ESTRO 38

Outcome-optimized plans were created for 38 patients (Figure 1). The maximum reduction of the total risk (summed probabilities of recurrence and normal tissue complication) achieved by outcome-optimized plans compared to clinical plans was 8.1% for patient cases with cardiac risk factors (CRF) and 10.9% for cases without CRF (Figure 2). The total risk was reduced more than 1% for 9/38 patient cases with CRF and 10/38 patient cases without CRF. The results are, however, sensitive to the definition of risk models, in particular models of tumor control under inhomogeneous target coverage.

Conclusion The robust optimization plans yielded higher doses compared to the CTV and more spared dose to the carotid artery compared to the PTV-based optimization plans. With respect to the perturbed evaluation, the doses to the carotid arteries and spinal cord showed less variation with the robust optimization plans compared to the PTV-based optimization plans. The robust optimization plan may be a suitable treatment method in radiotherapy for larynx cancer patient. EP-1812 Outcome-optimized radiotherapy planning using risk modeling for lymphoma – a preliminary study L. Rechner 1 , A. Modiri 2 , L.B. Stick 1 , M.V. Maraldo 1 , S.R. Rice 3 , A. Sawant 2 , S.M. Bentzen 4 , I.R. Vogelius 1 1 Rigshospitalet- University of Copenhagen, Department of Oncology- Section of Radiotherapy, Copenhagen, Denmark ; 2 University of Maryland- School of Medicine, Department of Radiation Oncology, Baltimore, USA ; 3 University of Maryland Medical Center, Radiation Oncology, Baltimore, USA ; 4 University of Maryland School of Medicine, Comprehensive Cancer Center and Department of Epidemiology and Public Health, Baltimore, USA Purpose or Objective In current radiotherapy planning, a set of predefined, aggregated, population-based dose-volume constraints are used to maximize the therapeutic ratio. In this study, we introduce a novel radiotherapy planning approach which instead aims to maximize overall survival by incorporating risk of recurrence and mortality from late effects into inverse optimization. Material and Methods We retrospectively analyzed 38 Hodgkin lymphoma patients (ages: 16-76) with mediastinal disease who were treated with 3D conformal radiotherapy. We used published data to develop risk models for lymphoma recurrence and late normal tissue complications, where we considered radiation-induced mortality from coronary heart disease, secondary lung cancer, and secondary breast cancer. Patient age, sex and cardiac risk factors (CRFs) as well as doses to heart, lung, breast, and tumor target were incorporated in the models. Patients were planned twice, once assuming the presence of CRFs and once assuming no CRFs. 16 co-planar gantry angles were chosen and 4 beam options per gantry angle were created and used for optimization. The dose from each beam option was calculated in a commercial treatment planning system and monitor units were optimized in our MATLAB- based, in-house, particle swarm optimization code to create outcome-optimized plans. Results

Figure 1. Example of a clinical (CLN) plan compared to an outcome-optimized (O-OPT) plan where the optimizer compromised the coverage of the target to spare the heart and lungs and improved risk of 9.9%.

Figure 2 . Reduction in risk (of recurrence and normal tissue complications) for outcome-optimized plans compared to clinical 3D conformal plans for the 38 patients with mediastinal Hodgkin lymphoma in this study with and without the presence of cardiac risk factors (CRF). Conclusion We present a study of an individualized outcome- optimized radiotherapy planning technique based on metrics related to overall survival. A substantial potential benefit was observed in some patients, but the underlying knowledge of dose-response models is an important limitation. EP-1813 AAA vs Monte Carlo Dose Calculation Algorithm for Lung SABR I. Badragan 1 , V. Huang 1 , D. Shellenberg 2 , A. Krauze 2 1 BC Cancer Surrey, Medical Physics, Surrey, Canada ; 2 BC Cancer Surrey, Radiation Oncology, Surrey, Canada