Higher doses are delivered over a short duration for stereotactic ablative radiotherapy (SABR) and as a result individual fraction times are significantly higher compared with conventional radiotherapy. Furthermore, many lung SABR patients are elderly with associated co-morbidities and may not be able to retain their treatment position adequately. These patients benefit from faster treatment deliveries which can be achieved by using flattening filter free (FFF) beams. To determine a clinically appropriate FFF energy for accurate delivery, 15 previously delivered flattened 6 MV lung SABR plans were re-planned at 6 FFF and 10 FFF, with organ at risk (OAR) and target dose-volume statistics examined for significance. A two half arc technique, the Monitor Unit Objective Function and the AcurosXB algorithm were employed within the Eclipse TM planning system (V11, Varian Medical System). The deliverability of these FFF plans was verified by physical measurement on a TrueBeam TM (V2.5, Varian Medical System) using the Compass TM dosimetry system (V3.1, IBA Dosimetry) in addition to the usual treatment planning system comparisons. Acceptable plans were produced for all beam energies. 6 FFF provided statistically significant OAR sparing compared to 6 FF and 10 FFF. However, absolute dose differences were not clinically significant and doses were well within recommended clinical tolerances. Skin sparing was superior in the 10 FFF plans. Overall, reduction in treatment delivery time of 61% and 55% was found when using 10 FFF and 6 FFF respectively compared to 6 FF. A 15% reduction in the average treatment time was achieved with 10 FFF when compared to 6 FFF. Treatment delivery verification measurements were compared with clinically delivered 6 FF plans and no significant differences in the deliverability were seen between the plans. As a result of this study 10 FFF has been implemented for SABR lung planning locally.

Objectives: Radiotherapy plan quality may vary considerably depending on planner’s experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics. Methods: Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement. Results: The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements. Conclusion: Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models. Advances in knowledge: In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

Objectives: anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome.Typically, patients who may benefit from adaptive planning are identified via a replanning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning.Methods: knowledge-based planning (KBP) models were developed using data for 20 patients' (400 fractions) to predict changes in PTV V95 coverage (∆𝑉95 𝑃𝑇𝑉 ). Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predict (∆𝑉95 𝑃𝑇𝑉 )and verified using five patients (100 fractions) data.Results: three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in 'planning' and 'fraction' PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting the (∆𝑉95 𝑃𝑇𝑉 )within ± 1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively. Conclusion:the KBP models can predict (∆𝑉95 𝑃𝑇𝑉 )very effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.

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Background Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. Methods This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. Discussion The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.