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Objective. This study aims to determine a margin and robustness setting for treating locally advanced cervical cancer (LACC) with a library-of-plans (LoP) based online-adaptive intensity-modulated proton therapy (IMPT). Approach. We analyzed 13 LACC patients with delineated planning and weekly repeat CT scans (reCTs). For each patient, 120 IMPT treatments of 25 fractions were simulated with a LoPs approach. Six different robustness settings (2-7 mm set-up robustness (SR) plus 3% range robustness (RR)) were used to create those 120 IMPT plans. Each fraction was simulated with a weekly reCT, combined with the sampling of inter- and intrafraction treatment uncertainties. The fraction doses were accumulated to obtain a treatment dose to the target volumes, distinguishing between the low-risk clinical target volume (CTV-T-LR) and the elective CTV (CTV-E). If one of the two targets obtained an adequate coverage for more than 90% of the treatments, different anisotropic margins were sampled on top of the robustness setting to the other target to obtain the Pareto-optimal margin in terms of adequate coverage versus increase in target volume. Main results. The percentage of treatments that reach the dose criterion V 42.75Gy > 95% for the CTV-T-LR was 22.3%, 28.5%, 51.2%, 73.1%, 85.3%, and 90.0% for 2, 3, 4, 5, 6, and 7 mm SR plus 3% RR and for the CTV-E, this percentage was 60.4%, 73.8%, 86.5%, 92.3%, 96.9%, and 98.5%. The Pareto-optimal margin combined with a 5 mm/3% robustness setting for the CTV-T-LR with an adequate coverage for >90% of the treatments was given by {0, 1, 0, 3, 3, 0} mm in the left, right, anterior, posterior, cranial, caudal direction. Significance. Our study evaluated combinations of robustness and anisotropic margin settings for IMPT for LACC. With 5 mm SR and 3% RR for CTV-E and CTV-T-LR plus a margin to the CTV-T-LR of {0, 1, 0, 3, 3, 0} mm in left, right, anterior, posterior, cranial, and caudal ensured an adequate coverage for >90% of the simulated IMPT treatments., (Creative Commons Attribution license.)

Objective. Oxygen depletion is generally believed to play an important role in the FLASH effect-a differential reduction of the radiosensitivity of healthy tissues, relative to that of the tumour under ultra-high dose-rate (UHDR) irradiation conditions. In proton therapy (PT) with pencil-beam scanning (PBS), the deposition of dose, and, hence, the degree of (radiolytic) oxygen depletion varies both spatially and temporally. Therefore, the resulting oxygen concentration and the healthy-tissue sparing effect through radiation-induced hypoxia varies both spatially and temporally as well. Approach. We propose and numerically solve a physical oxygen diffusion model to study these effects and their dependence on tissue parameters and the scan pattern in pencil-beam delivery. Since current clinical FLASH PT (FLASH-PT) is based on 250 MeV shoot-through (transmission) beams, for which dose and dose rate (DR) hardly vary with depth compared to the variation transverse to the beam axis, we focus on the two-dimensional case. We numerically integrate the model to obtain the oxygen concentration in each voxel as a function of time and extract voxel-based and spatially and temporarily integrated metrics for oxygen (FLASH) enhanced dose. Furthermore, we evaluate the impact on oxygen enhancement of standard pencil-beam delivery patterns and patterns that were optimised on dose-rate. Our model can contribute to the identification of tissue properties and pencil-beam delivery parameters that are critical for FLASH-PT and it may be used for the optimisation of FLASH-PT treatment plans and their delivery. Main results. (i) the diffusive properties of oxygen are critical for the steady state concentration and therefore the FLASH effect, even more so in two dimensions when compared to one dimension. (ii) The FLASH effect through oxygen depletion depends primarily on dose and less on other parameters. (iii) At a fixed fraction dose there is a slight dependence on DR. (iv) Scan patterns optimised on DR slightly increase the oxygen induced FLASH effect. Significance. To our best knowledge, this is the first study assessing the impact of scan-pattern optimization (SPO) in FLASH-PT with PBS on a biological FLASH model. While the observed impact of SPO is relatively small, a larger effect is expected for larger target volumes. A better understanding of the FLASH effect and the role of oxygen (depletion) therein is essential for the further development of FLASH-PT with PBS, and SPO., (Creative Commons Attribution license.)

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use., (Creative Commons Attribution license.)

Objective . Increasing cancer incidence, staff shortage and high burnout rate among radiation oncologists, medical physicists and radiation technicians are putting many departments under strain. Operations research (OR) tools could optimize radiotherapy processes, however, clinical implementation of OR-tools in radiotherapy is scarce since most investigated optimization methods lack robustness against patient-to-patient variation in duration of tasks. By combining OR-tools, a method was developed that optimized deployment of radiotherapy resources by generating robust pretreatment preparation schedules that balance the expected average patient preparation time ( F mean ) with the risk of working overtime ( RoO ). The method was evaluated for various settings of an one-stop shop (OSS) outpatient clinic for palliative radiotherapy. Approach . The OSS at our institute sees, scans and treats 3-5 patients within one day. The OSS pretreatment preparation workflow consists of a fixed sequence of tasks, which was manually optimized for radiation oncologist and CT availability. To find more optimal sequences, with shorter F mean and lower RoO , a genetic algorithm was developed which regards these sequences as DNA-strands. The genetic algorithm applied natural selection principles to produce new sequences. A decoder translated sequences to schedules to find the conflicting fitness parameters F mean and RoO . For every generation, fitness of sequences was determined by the distance to the estimated Pareto front of F mean and RoO . Experiments were run in various OSS-settings. Main results . According to our approach, the expected F mean of the current clinical schedule could be reduced with 37%, without increasing RoO . Additional experiments provided insights in trade-offs between F mean , RoO , working shift length, number of patients treated on a single day and staff composition. Significance . Our approach demonstrated that OR-tools could optimize radiotherapy resources by robust pretreatment workflow scheduling. The results strongly support further exploration of scheduling optimization for treatment preparation also outside a one-stop shop or radiotherapy setting., (© 2024 Institute of Physics and Engineering in Medicine.)