The Exradin W1 response is energy independent for high energy x-rays and electron beams, and only one calibration coefficient is needed. A temperature correction factor should be applied to keep uncertainties around 2% for absolute dose measurements and around 1% for relative measurements in high-energy photon and electron beams. The Exradin W1 scintillator is an excellent alternative to detectors such as diodes for relative dose measurements.

An extensive set of benchmark measurement of PDDs and beam profiles was performed in a heterogeneous layer phantom, including a lung equivalent heterogeneity, by means of several detectors and compared against the predicted dose values by different calculation algorithms in two treatment planning systems. PDDs were measured with TLDs, plane parallel and cylindrical ionization chambers and beam profiles with films. Additionally, Monte Carlo simulations by means of the PENELOPE code were performed. Four different field sizes (10 x 10, 5 x 5, 2 x 2, and 1 x 1 cm2) and two lung equivalent materials (CIRS, p(w)e=0.195 and St. Bartholomew Hospital, London, p(w)e=0.244-0.322) were studied. The performance of four correction-based algorithms and one based on convolution-superposition was analyzed. The correction-based algorithms were the Batho, the Modified Batho, and the Equivalent TAR implemented in the Cadplan (Varian) treatment planning system and the TMS Pencil Beam from the Helax-TMS (Nucletron) treatment planning system. The convolution-superposition algorithm was the Collapsed Cone implemented in the Helax-TMS. The only studied calculation methods that correlated successfully with the measured values with a 2% average inside all media were the Collapsed Cone and the Monte Carlo simulation. The biggest difference between the predicted and the delivered dose in the beam axis was found for the EqTAR algorithm inside the CIRS lung equivalent material in a 2 x 2 cm2 18 MV x-ray beam. In these conditions, average and maximum difference against the TLD measurements were 32% and 39%, respectively. In the water equivalent part of the phantom every algorithm correctly predicted the dose (within 2%) everywhere except very close to the interfaces where differences up to 24% were found for 2 x 2 cm2 18 MV photon beams. Consistent values were found between the reference detector (ionization chamber in water and TLD in lung) and Monte Carlo simulations, yielding minimal differences (0.4%+/-1.2%). The penumbra broadening effect in low density media was not predicted by any of the correction-based algorithms, and the only one that matched the experimental values and the Monte Carlo simulations within the estimated uncertainties was the Collapsed Cone Algorithm.

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

We found no correlation between the gamma index and the clinical impact of a discrepancy for any of the gamma index evaluation possibilities (global, local, 2D, or 3D). Some of the tests yielded false positives or false negatives in a per-beam gamma analysis. However, they were correctly accounted for in a DVH analysis. We also showed that 3DVH software is reliable for our tests, and is a viable method for correlating planar discrepancies with clinical relevance by comparing the measured DVH of target and OAR's with clinical tolerance.

Background and purpose: It is known that intensity-modulated radiotherapy plans that are highly complex might be less accurate in dose calculation and treatment delivery. Multiple complexity metrics have been proposed, but the relationships between them have not been thoroughly investigated. This study investigated these relationships in multi-institutional comparisons of treatment plans, where plans from multiple treatment planning systems (TPSs) are typically evaluated. Materials and methods: A program was developed to compute several complexity indices and provide analysis of dynamic plan parameters. This in-house software was used to analyse plans from a recent multi-institutional audit. Additionally, 100 clinical volumetric modulated arc therapy (VMAT) plans from two institutions using different TPSs were analysed. Results: All plans produced satisfactory pre-treatment verification results and, hence, complexity metrics could not be used to predict plans failing QA. Regarding the relationship among complexity indices, some very strong correlations were found (r > 0.9 with p < 0.01). However, some relevant discrepancies between complexity indices were obtained, even with negative correlation coefficients (r ∼ −0.6) which were expected to be positive. These discrepancies could be explained because each complexity index focused on different features of the plan and different TPSs prioritised modulation of different plan parameters. Conclusions: Some complexity indices provided similar information and can be considered equivalent. However, indices that focused on different plan parameters yielded different results and it was unclear which complexity index should be used. Careful consideration should be given to the use of complexity metrics in multi-institutional studies.