The accuracy of dose calculation is a key challenge in stereotactic body radiotherapy (SBRT) of the lung. We have benchmarked three photon beam dose calculation algorithms — pencil beam convolution (PBC), anisotropic analytical algorithm (AAA), and Acuros XB (AXB) — implemented in a commercial treatment planning system (TPS), Varian Eclipse. Dose distributions from full Monte Carlo (MC) simulations were regarded as a reference. In the first stage, for four patients with central lung tumors, treatment plans using 3D conformal radiotherapy (CRT) technique applying 6 MV photon beams were made using the AXB algorithm, with planning criteria according to the Nordic SBRT study group. The plans were recalculated (with same number of monitor units (MUs) and identical field settings) using BEAMnrc and DOSXYZnrc MC codes. The MC‐calculated dose distributions were compared to corresponding AXB‐calculated dose distributions to assess the accuracy of the AXB algorithm, to which then other TPS algorithms were compared. In the second stage, treatment plans were made for ten patients with 3D CRT technique using both the PBC algorithm and the AAA. The plans were recalculated (with same number of MUs and identical field settings) with the AXB algorithm, then compared to original plans. Throughout the study, the comparisons were made as a function of the size of the planning target volume (PTV), using various dose‐volume histogram (DVH) and other parameters to quantitatively assess the plan quality. In the first stage also, 3D gamma analyses with threshold criteria 3normal%/3 mm and 2normal%/2 mm were applied. The AXB‐calculated dose distributions showed relatively high level of agreement in the light of 3D gamma analysis and DVH comparison against the full MC simulation, especially with large PTVs, but, with smaller PTVs, larger discrepancies were found. Gamma agreement index (GAI) values between 95.5% and 99.6% for all the plans with the threshold criteria 3normal%/3 mm were achieved, but 2normal%/2 mm threshold criteria showed larger discrepancies. The TPS algorithm comparison results showed large dose discrepancies in the PTV mean dose false(normalD50normal%false), nearly 60%, for the PBC algorithm, and differences of nearly 20% for the AAA, occurring also in the small PTV size range. This work suggests the application of independent plan verification, when the AAA or the AXB algorithm are utilized in lung SBRT having PTVs smaller than 20‐25 cc. The calculated data from this study can be used in converting the SBRT protocols based on type ‘a’ and/or type ‘b’ algorithms for the most recent generation type ‘c’ algorithms, such as the AXB algorithm.PACS numbers: 87.55.‐x, 87.55.D‐, 87.55.K‐, 87.55.kd, 87.55.Qr

In this study, the clinical benefit of the improved accuracy of the Acuros XB (AXB) algorithm, implemented in a commercial radiotherapy treatment planning system (TPS), Varian Eclipse, was demonstrated with beams traversing a high‐Z material. This is also the first study assessing the accuracy of the AXB algorithm applying volumetric modulated arc therapy (VMAT) technique compared to full Monte Carlo (MC) simulations. In the first phase the AXB algorithm was benchmarked against point dosimetry, film dosimetry, and full MC calculation in a water‐filled anthropometric phantom with a unilateral hip implant. Also the validity of the full MC calculation used as reference method was demonstrated. The dose calculations were performed both in original computed tomography (CT) dataset, which included artifacts, and in corrected CT dataset, where constant Hounsfield unit (HU) value assignment for all the materials was made. In the second phase, a clinical treatment plan was prepared for a prostate cancer patient with a unilateral hip implant. The plan applied a hybrid VMAT technique that included partial arcs that avoided passing through the implant and static beams traversing the implant. Ultimately, the AXB‐calculated dose distribution was compared to the recalculation by the full MC simulation to assess the accuracy of the AXB algorithm in clinical setting. A recalculation with the anisotropic analytical algorithm (AAA) was also performed to quantify the benefit of the improved dose calculation accuracy of type ‘c’ algorithm (AXB) over type ‘b’ algorithm (AAA). The agreement between the AXB algorithm and the full MC model was very good inside and in the vicinity of the implant and elsewhere, which verifies the accuracy of the AXB algorithm for patient plans with beams traversing through high‐Z material, whereas the AAA produced larger discrepancies.PACS numbers: 87.55.‐x, 87.55.D‐, 87.55.K‐, 87.55.kd, 87.55.Qr

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The purpose of this work is to evaluate the predictive strength of the relative seriality, parallel and LKB normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis, in a large group of patients following breast cancer radiotherapy, and furthermore, to illustrate statistical methods for examining whether certain published radiobiological parameters are compatible with a clinical treatment methodology and patient group characteristics. The study is based on 150 consecutive patients who received radiation therapy for breast cancer. For each patient, the 3D dose distribution delivered to lung and the clinical treatment outcome were available. Clinical symptoms and radiological findings, along with a patient questionnaire, were used to assess the manifestation of radiation-induced complications. Using this material, different methods of estimating the likelihood of radiation effects were evaluated. This was attempted by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Additionally, the need for an update of the criteria that are being used in the current clinical practice was also examined. The patient material was selected without any conscious bias regarding the radiotherapy treatment technique used. The treatment data of each patient were applied to the relative seriality, LKB and parallel NTCP models, using published parameter sets. Of the 150 patients, 15 experienced radiation-induced pneumonitis (grade 2) according to the radiation pneumonitis scoring criteria used. Of the NTCP models examined, the relative seriality model was able to predict the incidence of radiation pneumonitis with acceptable accuracy, although radiation pneumonitis was developed by only a few patients. In the case of modern breast radiotherapy, radiobiological modelling appears to be very sensitive to model and parameter selection giving clinically acceptable results in certain cases selectively (relative seriality model with Seppenwoolde et al and Gagliardi et al parameter sets). The use of published parameters should be considered as safe only after their examination using local clinical data. The variation of inter-patient radiosensitivity seems to play a significant role in the prediction of such low incidence rate complications. Scoring grades were combined to give stronger evidence of radiation pneumonitis since their differences could not be strictly associated with dose. This obviously reveals a weakness of the scoring related to this endpoint, and implies that the probability of radiation pneumonitis induction may be too low to be statistically analysed with high accuracy, at least with the latest advances of dose delivery in breast radiotherapy.

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