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Background and purpose: Radiotherapy planning considerations were developed for the previous calculation algorithms yielding dose to water-in-water (Dw,w). Advanced algorithms improve accuracy, but their dose values in terms of dose to medium-in-medium (Dm,m) depend on the medium considered. This work aimed to show how mimicking Dw,w planning with Dm,m can introduce new issues. Materials and methods: A head and neck case involving bone and metal heterogeneities outside the CTV was considered. Two different commercial algorithms were used to obtain Dm,m and Dw,w distributions. First, a plan was optimised to irradiate the PTV uniformly and get a homogeneous Dw,w distribution. Second, another plan was optimised to achieve homogeneous Dm,m. Both plans were calculated with Dw,w and Dm,m, and the differences between their dose distributions, clinical impact, and robustness were evaluated. Results: Uniform irradiation produced Dm,m cold spots in bone (-4%) and implants (-10%). Uniform Dm,m compensated them by increasing fluence but, when recalculated in Dw,w, the fluence compensations produced higher doses that affected homogeneity. Additionally, doses were 1% higher for the target, and + 4% for the mandible, thus increasing toxicity risk. Robustness was impaired when increased fluence regions and heterogeneities mismatched. Conclusion: Planning with Dm,m as with Dw,w can impact clinical outcome and impair robustness. In optimisation, uniform irradiation instead of homogeneous Dm,m distributions should be pursued when media with different Dm,m responses are involved. However, this requires adapting evaluation criteria or avoiding medium effects. Regardless of the approach, there can be systematic differences in dose prescription and constraints.

Abstract To assess adherence to standard clinical practice for the diagnosis and treatment of patients undergoing prostate cancer (PCa) radiotherapy in four European countries using clinical audits as part of the international IROCA project. Multi-institutional, retrospective cohort study of 240 randomly-selected patients treated for PCa (n = 40/centre) in the year 2015 at six European hospitals. Clinical indicators applicable to general and PCa-specific radiotherapy processes were evaluated. All data were obtained directly from medical records. The audits were performed in the year 2017. Adherence to clinical protocols and practices was satisfactory, but with substantial inter-centre variability in numerous variables, as follows: staging MRI (range 27.5–87.5% of cases); presentation to multidisciplinary tumour board (2.5–100%); time elapsed between initial visit to the radiation oncology department and treatment initiation (42–102.5 days); number of treatment interruptions ≥ 1 day (7.5–97.5%). The most common deviation from standard clinical practice was inconsistent data registration, mainly failure to report data related to diagnosis, treatment, and/or adverse events. This clinical audit detected substantial inter-centre variability in adherence to standard clinical practice, most notably inconsistent record keeping. These findings confirm the value of performing clinical audits to detect deviations from standard clinical practices and procedures.

Purpose: Our purpose was to assess the dosimetric effect of switching from the analytical anisotropic algorithm (AAA) to Acuros XB (AXB), with dose-to-medium (Dm) and dose-to-water (Dw) reporting modes, in lung stereotactic body radiation therapy patients and determine whether planning-target-volume (PTV) dose prescriptions and organ-at-risk constraints should be modified under these circumstances. Methods and Materials: We included 54 lung stereotactic body radiation therapy patients. We delineated the PTV, the ipsilateral lung, the contralateral lung, the heart, the spinal cord, the esophagus, the trachea, proximal bronchi, the ribs, and the great vessels. We performed dose calculations with AAA and AXB, then compared clinically relevant dose-volume parameters. Paired t tests were used to analyze differences of means. We propose a method, based on the composition of the involved structures, for predicting differences between AXB Dw and Dm calculations. Results: The largest difference between the algorithms was 4%. Mean dose differences between AXB Dm and AXB Dw depended on the average composition of the volumes. Compared with AXB, AAA underestimated all PTV dose-volume parameters (-0.7 Gy to -0.1 Gy) except for gradient index, which was significantly higher (4%). It also underestimated V5 of the contralateral lung (-0.3%). Significant differences in near-maximum doses (D2) to the ribs were observed between AXB Dm and AAA (1.7%) and between AXB Dw and AAA (-1.6%). AAA-calculated D2 was slightly higher in the remaining organs at risk. Conclusions: Differences between AXB and AAA are below the threshold of clinical detectability (5%) for most patients. For a small subgroup, the difference in maximum doses to the ribs between AXB Dw and AXB Dm may be clinically significant. The differences in dose volume parameters between AXB Dw and AXB Dm can be predicted with reference to structure composition.

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