ESTRO 36 Abstract Book

S437 ESTRO 36 2017 _______________________________________________________________________________________________

quality. Furthermore this approach enables the identification of problematic plans beforehand. PO-0822 An evolutionary model improvement strategy for knowledge-based planning Y. Zhang 1 , F. Jiang 1 , H. Yue 1 , S. Li 1 , Q. Hu 1 , M. Wang 2 , H. Wu 1 1 Key Laboratory of Carcinogenesis and Translational Research Ministry of Education/Beijing- Department of Radiation Oncology- Peking University Cancer Hospital & Institute, Department of Radiation Oncology, Beijing, China 2 National Institute for Radiological Protection- China CDC, National Institute for Radiological Protection-, Beijing, China Purpose or Objective It was reported that RapidPlan, a knowledge-based solution, can improve planning efficiency, quality and consistency. Should the performance of RapidPlan be dependent on the quality of the plans training the model, this study hypothesizes that RapidPlan can improve its constituent plans (closed-loop) and improve the model itself by incorporating the better knowledge (evolution). Moreover, the maximum number of iterations to exhaust the full potential was also tested. Material and Methods An initial RapidPlan model (M 0 ) for pre-surgical rectal cancer patients was configured using 81 best-effort manual VMAT plans (P 0 ) with SIB (50.6 Gy and 41.8 Gy to 95% PTV boost and PTV respectively). For simplification, decreased or increased mean dose to both femoral head (FH) and urinary bladder (UB) were considered as improved (P + ) or worsened plans (P - ) respectively. P ± denoted intertwined plans. The first closed-loop iteration of re-optimizing P 0 using M 0 yielded P 1 : 69 P 1+ , 12 P 1± and 0 P 1- . By substituting P 1+ for their corresponding P 0 , the library of model M 1+ consisted of 12 P 0 and 69 P 1+ . The second closed-loop iteration of re-optimizing P 0 using M 1+ produced 35 P 2+ that were superior to both P 0 and P 1 , hence the knowledge base of M 2+ composed 9 P 0 , 37 P 1+ and 35 P 2+ . As open-loop validation, 30 clinical plans (P v ) that were not included in the model were re-optimized using each model. Re-optimization maintained all parameters except the MLC sequences were redesigned using the objectives generated by the models. Renormalizations to target prescriptions were performed to make OAR dose comparable. Results Consistent with literature, knowledge-based so lution improved the plan quality in both closed- and open-loop validations than the conventional trial-and-error process. In the first closed-loop evolution, the mean±SD of D mean_FH and D mean_UB for 69 P 1+ were 12.88±1.38 Gy and 23.06±3.11 Gy respectively, which were significantly lower by 23.70% and 9.53% than the corresponding values of P 0 (P<0.05). In the second round of closed-loop re-optimization, the D mean_FH of the 35 P 2+ decreased to 11.12±1.48 Gy (corresponding P 0 : 17.13±2.06 Gy; P 1 : 13.08±1.36 Gy), and D mean_UB decreased to 22.80±3.72 Gy (corresponding P 0 : 25.79±3.34 Gy; P 1 : 23.41±3.59 Gy). Table 1 and figure 1 display the open-loop validation results for various models. The marginal disparities of HI and CI (magnitudes ≦ 0.04) and largely overlapped DVH lines indicated comparable target dose distribution. In line with the closed-loop test, RapidPlan reduced the dose to OARs massively than clinical plans in the open-loop validation (Fig 1). The first model evolution has greatly and significantly lowered the dose to FH at comparable dose to the UB and targets. The second iteration has made little difference though.

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,