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The purpose of this study is to investigate the influence of lung heterogeneity inside a soft tissue phantom on percentage depth dose (PDD). PDD curves were obtained experimentally using LiF:Mg,Ti (TLD‐100) thermoluminescent detectors and applying Eclipse treatment planning system algorithms Batho, modified Batho (M‐Batho or BMod), equivalent TAR (E‐TAR or EQTAR), and anisotropic analytical algorithm (AAA) for a 15 MV photon beam and field sizes of 1×1,.2em2×2,.2em5×5, and 10×10.2emcm2. Monte Carlo simulations were performed using the DOSRZnrc user code of EGSnrc. The experimental results agree with Monte Carlo simulations for all irradiation field sizes. Comparisons with Monte Carlo calculations show that the AAA algorithm provides the best simulations of PDD curves for all field sizes investigated. However, even this algorithm cannot accurately predict PDD values in the lung for field sizes of 1×1 and 2×2.2emcm2. An overdosage in the lung of about 40% and 20% is calculated by the AAA algorithm close to the interface soft tissue/lung for 1×1 and 2×2.2emcm2 field sizes, respectively. It was demonstrated that differences of 100% between Monte Carlo results and the algorithms Batho, modified Batho, and equivalent TAR responses may exist inside the lung region for the 1×1.2emcm2 field.PACS number: 87.55.kd

Introduction: Within an International Atomic Energy Agency (IAEA) coordinated research project (CRP), a remote end-to-end dosimetric quality audit for intensity modulated radiation therapy (IMRT)/ volumetric arc therapy (VMAT) was developed to verify the radiotherapy chain including imaging, treatment planning and dose delivery. The methodology as well as the results obtained in a multicentre pilot study and national trial runs conducted in close cooperation with dosimetry audit networks (DANs) of IAEA Member States are presented. Material and methods: A solid polystyrene phantom containing a dosimetry insert with an irregular solid water planning target volume (PTV) and organ at risk (OAR) was designed for this audit. The insert can be preloaded with radiochromic film and four thermoluminescent dosimeters (TLDs). For the audit, radiotherapy centres were asked to scan the phantom, contour the structures, create an IMRT/VMAT treatment plan and irradiate the phantom. The dose prescription was to deliver 4 Gy to the PTV in two fractions and to limit the OAR dose to a maximum of 2.8 Gy. The TLD measured doses and film measured dose distributions were compared with the TPS calculations. Results: Sixteen hospitals from 13 countries and 64 hospitals from 6 countries participated in the multicenter pilot study and in the national runs, respectively. The TLD results for the PTV were all within ±5% acceptance limit for the multicentre pilot study, whereas for national runs, 17 participants failed to meet this criterion. All measured doses in the OAR were below the treatment planning constraint. The film analysis identified seven plans in national runs below the 90% passing rate gamma criteria. Conclusion: The results proved that the methodology of the IMRT/VMAT dosimetric end-to-end audit was feasible for its intended purpose, i.e., the phantom design and materials were suitable; the phantom was easy to use and it was robust enough for shipment. Most importantly the audit methodology was capable of identifying suboptimal IMRT/VMAT delivery.

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Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50–0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.