A study of the performance of five commercial radiotherapy treatment planning systems (TPSs) for common treatment sites regarding their ability to model heterogeneities and scattered photons has been performed. The comparison was based on CT information for prostate, head and neck, breast and lung cancer cases. The TPSs were installed locally at different institutions and commissioned for clinical use based on local procedures. For the evaluation, beam qualities as identical as possible were used: low energy (6 MV) and high energy (15 or 18 MV) x-rays. All relevant anatomical structures were outlined and simple treatment plans were set up. Images, structures and plans were exported, anonymized and distributed to the participating institutions using the DICOM protocol. The plans were then re-calculated locally and exported back for evaluation. The TPSs cover dose calculation techniques from correction-based equivalent path length algorithms to model-based algorithms. These were divided into two groups based on how changes in electron transport are accounted for ((a) not considered and (b) considered). Increasing the complexity from the relatively homogeneous pelvic region to the very inhomogeneous lung region resulted in less accurate dose distributions. Improvements in the calculated dose have been shown when models consider volume scatter and changes in electron transport, especially when the extension of the irradiated volume was limited and when low densities were present in or adjacent to the fields. A Monte Carlo calculated algorithm input data set and a benchmark set for a virtual linear accelerator have been produced which have facilitated the analysis and interpretation of the results. The more sophisticated models in the type b group exhibit changes in both absorbed dose and its distribution which are congruent with the simulations performed by Monte Carlo-based virtual accelerator.

A comparative study was performed to reveal differences and relative figures of merit of seven different calculation algorithms for photon beams when applied to inhomogeneous media. The following algorithms were investigated: Varian Eclipse: the anisotropic analytical algorithm, and the pencil beam with modified Batho correction; Nucletron Helax-TMS: the collapsed cone and the pencil beam with equivalent path length correction; CMS XiO: the multigrid superposition and the fast Fourier transform convolution; Philips Pinnacle: the collapsed cone. Monte Carlo simulations (MC) performed with the EGSnrc codes BEAMnrc and DOSxyznrc from NRCC in Ottawa were used as a benchmark. The study was carried out in simple geometrical water phantoms (rho = 1.00 g cm(-3)) with inserts of different densities simulating light lung tissue (rho = 0.035 g cm(-3)), normal lung (rho = 0.20 g cm(-3)) and cortical bone tissue (rho = 1.80 g cm(-3)). Experiments were performed for low- and high-energy photon beams (6 and 15 MV) and for square (13 x 13 cm2) and elongated rectangular (2.8 x 13 cm2) fields. Analysis was carried out on the basis of depth dose curves and transverse profiles at several depths. Assuming the MC data as reference, gamma index analysis was carried out distinguishing between regions inside the non-water inserts or inside the uniform water. For this study, a distance to agreement was set to 3 mm while the dose difference varied from 2% to 10%. In general all algorithms based on pencil-beam convolutions showed a systematic deficiency in managing the presence of heterogeneous media. In contrast, complicated patterns were observed for the advanced algorithms with significant discrepancies observed between algorithms in the lighter materials (rho = 0.035 g cm(-3)), enhanced for the most energetic beam. For denser, and more clinical, densities a better agreement among the sophisticated algorithms with respect to MC was observed.

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Treatment planning is time‐consuming and the outcome depends on the person performing the optimization. A system that automates treatment planning could potentially reduce the manual time required for optimization and could also provide a method to reduce the variation between persons performing radiation dose planning (dosimetrist) and potentially improve the overall plan quality. This study evaluates the performance of the Auto‐Planning module that has recently become clinically available in the Pinnacle3 radiation therapy treatment planning system. Twenty‐six clinically delivered head and neck treatment plans were reoptimized with the Auto‐Planning module. Comparison of the two types of treatment plans were performed using DVH metrics and a blinded clinical evaluation by two senior radiation oncologists using a scale from one to six. Both evaluations investigated dose coverage of target and dose to healthy tissues. Auto‐Planning was able to produce clinically acceptable treatment plans in all 26 cases. Target coverages in the two types of plans were similar, but automatically generated plans had less irradiation of healthy tissue. In 94% of the evaluations, the autoplans scored at least as high as the previously delivered clinical plans. For all patients, the Auto‐Planning tool produced clinically acceptable head and neck treatment plans without any manual intervention, except for the initial target and OAR delineations. The main benefit of the method is the likely improvement in the overall treatment quality since consistent, high‐quality plans are generated which even can be further optimized, if necessary. This makes it possible for the dosimetrist to focus more time on difficult dose planning goals and to spend less time on the more tedious parts of the planning process.PACS number: 87.55.de

HighlightsAutoplan can produce clinically better VMAT radiotherapy plans for H&N cancer.Autoplan plans have similar target coverage with significant sparing of OAR.With a template the TPS can automatically create better plans than manually created plans.

One training dataset of 132 and two validation datasets of 62 and 94stage I-IV NSCLC patients were included. Interchangeability was assessed by performing a linear regression on CT and CBCT extracted features. A two-step correction was applied prior to model validation of a previously published radiomic signature. Results 13.3% (149 out of 1119) of the radiomic features, including all features of the previously published radiomic signature, showed an R above 0.85 between intermodal imaging techniques. For the radiomic signature, Kaplan-Meier curves were significantly different between groups with high and low prognostic value for both modalities. Harrell's concordance index was 0.69 for CT and 0.66 for CBCT models for dataset 1. Conclusions The results show that a subset of radiomic features extracted from CT and CBCT images are interchangeable using simple linear regression. Moreover, a previously developed radiomics signature has prognostic value for overall survival in three CBCT cohorts, showing the potential of CBCT radiomics to be used as prognostic imaging biomarker.

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