Abstract

BackgroundAutomated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality. Five different ATPS used clinically were evaluated for advanced head and neck cancer (HNC).MethodsThree radiation oncology departments compared 5 different ATPS: 1) Automatic Interactive Optimizer (AIO) in combination with RapidArc (in-house developed and Varian Medical Systems); 2) Auto-Planning (AP) (Philips Radiation Oncology Systems); 3) RapidPlan version 13.6 (RP1) with HNC model from University Hospital A (Varian Medical Systems, Palo Alto, USA); 4) RapidPlan version 13.7 (RP2) combined with scripting for automated setup of fields with HNC model from University Hospital B; 5) Raystation multicriteria optimization algorithm version 5 (RS) (Laboratories AB, Stockholm, Sweden). Eight randomly selected HNC cases from institution A and 8 from institution B were used. PTV coverage, mean and maximum dose to the organs at risk and effective planning time were compared. Ranking was done based on 3 Gy increments for the parallel organs.ResultsAll planning systems achieved the hard dose constraints for the PTVs and serial organs for all patients. Overall, AP achieved the best ranking for the parallel organs followed by RS, AIO, RP2 and RP1. The oral cavity mean dose was the lowest for RS (31.3 ± 17.6 Gy), followed by AP (33.8 ± 17.8 Gy), RP1 (34.1 ± 16.7 Gy), AIO (36.1 ± 16.8 Gy) and RP2 (36.3 ± 16.2 Gy). The submandibular glands mean dose was 33.6 ± 10.8 Gy (AP), 35.2 ± 8.4 Gy (AIO), 35.5 ± 9.3 Gy (RP2), 36.9 ± 7.6 Gy (RS) and 38.2 ± 7.0 Gy (RP1). The average effective planning working time was substantially different between the five ATPS (in minutes): < 2 ± 1 for AIO and RP2, 5 ± 1 for AP, 15 ± 2 for RP1 and 116 ± 11 for RS, respectively.ConclusionsAll ATPS were able to achieve all planning DVH constraints and the effective working time was kept bellow 20 min for each ATPS except for RS. For the parallel organs, AP performed the best, although the differences were small.

Highlights

  • Automated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality

  • Study design In this multi-institutional planning study, five automated treatment planning systems used in 3 different institutes were evaluated: 1) Automatic Interactive Optimizer (AIO) in combination with RapidArc version 13.7 from Eclipse (Varian Medical Systems, Palo Alto, USA) from hospital B [4, 12]; 2) Auto-Planning version 14.0 (AP) from Pinnacle (Philips Radiation Oncology Systems) from hospital A [6]; 3) RapidPlan version 13.6 (RP1) from Eclipse (Varian Medical Systems, Palo Alto, USA) using head and neck cancer (HNC) model from hospital A; 4) RapidPlan version 13.7 (RP2) combined with scripting for automated setup of fields with HNC model from hospital B [13]; 5) Raystation multicriteria optimization algorithm version 5 (RS) (RaySearch Laboratories AB, Stockholm, Sweden), from hospital C

  • V50 Volume receiving X Gy (Gy) and V30 Gy were increased in comparison to RS by 1.9 ± 10.6% and 12.9 ± 18.0% (RP1), 6.1 ± 10.4% and 27.0 ± 13.7% (RP2), 8.7 ± 12.4% and 33.2 ± 13.0% (AIO), 18.5 ± 17.6% and 23.9 ± 17.8% (AP)

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Summary

Introduction

Automated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality. Five different ATPS used clinically were evaluated for advanced head and neck cancer (HNC). The inverse optimization approach is an iterative process where optimization objectives are used in order to achieve the pre-defined clinical goals. The complexity of the optimization increases with the number of organ at risks (OAR) and the number of target volumes. Head and neck carcinoma (HNC) is a typical complex case where a large number of OARs, typically 10–20, are surrounding the target volumes irradiated to different dose levels. This makes inverse planning optimization one of the most time consuming steps of the overall treatment planning process

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