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.

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.

Low-dose radiotherapy (LD-RT) has been used for several benign diseases, including arthrodegenerative and inflammatory pathologies. Despite its effectiveness in clinical practice, little is known about the mechanisms through which LD-RT modulates the various phases of the inflammatory response and about the optimal dose fractionation. The objective of this review is to deepen knowledge about the most effective LD-RT treatment schedule and radiobiological mechanisms underlying the anti-inflammatory effects of LD-RT in various in vitro experiments, in vivo studies, and clinical studies.

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

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Modelling of the multi-leaf collimator (MLC) in treatment planning systems (TPS) is crucial for the dose calculation accuracy of intensity-modulated radiation therapy plans. However, no standardised methodology for their configuration exists to date. In this study we present a method that separates the effect of each dosimetric characteristic of the MLC, offering comprehensive equations for the determination of the configuration parameters used in the TPS model. The main advantage of the method is that it only requires prior knowledge of the nominal leaf width and is based on doses measured with a Farmer chamber, which is a very well established and robust methodology. Another significant advantage is the required time, since measuring the tests takes only about 30 minutes per energy. Firstly, we provide a theoretical general formalism in terms of the primary fluence constructed from the transmission map obtained from an MLC model for synchronous and asynchronous sweeping beams. Secondly, we apply the formalism to the RayStation TPS as a proof of concept and we derive analytical expressions that allow the determination of the configuration parameters (leaf tip width, tongue-and-groove width, x-position offset and MLC transmission) and describe how they intertwine. Finally, we apply the method to Varian’s Millennium120 and HD120 MLCs in a TrueBeam linear accelerator for different energies and determine the optimal configuration parameters. The proposed procedure is much faster and streamlined than the typical trial-and-error methods and increases the accuracy of dose calculation in clinical plans. Additionally, the procedure can be useful for standardising the MLC configuration process and it exposes the limitations of the implemented MLC model, providing guidance for further improvement of these models in TPSs.

Adequate modelling of the multi-leaf collimator (MLC) by treatment planning systems (TPS) is essential for accurate dose calculations in intensity-modulated radiation-therapy. For this reason modern TPSs incorporate MLC characteristics such as the leaf end curvature, MLC transmission and the tongue-and-groove. However, the modelling of the tongue-and-groove is often neglected during TPS commissioning and it is not known how accurate it is. This study evaluates the dosimetric consequences of the tongue-and-groove effect for two different MLC models using both film dosimetry and ionisation chambers. A set of comprehensive tests are presented that evaluate the ability of TPSs to accurately model this effect in (a) static fields, (b) sliding window beams and (c) VMAT arcs. The tests proposed are useful for the commissioning of TPSs and for the validation of major upgrades. With the ECLIPSE TPS, relevant differences were found between calculations and measurements for beams with dynamic MLCs in the presence of the TG effect, especially for the High Definition MLC, small gap sizes and the 1 mm calculation grid. For this combination, dose differences as high as 10% and 7% were obtained for dynamic MLC gaps of 5 mm and 10 mm, respectively. These differences indicate inadequate modelling of the tongue-and-groove effect, which might not be identified without the proposed tests. In particular, the TPS tended to underestimate the calculated dose, which may require tuning of other configuration parameters in the TPS (such as the dosimetric leaf gap) in order to maximise the agreement between calculations and measurements in clinical plans. In conclusion, a need for better modelling of the MLC by TPSs is demonstrated, one of the relevant aspects being the tongue-and-groove effect. This would improve the accuracy of TPS calculations, especially for plans using small MLC gaps, such as plans with small target volumes or high complexities. Improved modelling of the MLC would also reduce the need for tuning parameters in the TPS, facilitating a more comprehensive configuration and commissioning of TPSs.

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