Objective: Scenario-based evaluation in proton therapy often relies on a small number of error scenarios, leading to limited insight into the DVH values under uncertainty and suboptimal trade-offs. In this study, we investigated if re-optimization based on probabilistic evaluation improves the trade-off between OAR sparing and target coverage in neuro-oncological patients. Materials and methods: 22 neuro-oncological patients were included. 18 met their original target goals (group A), while in 4, target coverage was compromised to spare OARs (group B). The probabilistic goal for the CTV was calibrated to be consistent with PTV-based photon plans, resulting in D_99.8%,CTV = 0.95D_pres with a 90 % confidence level. The probabilistic OAR constraints were set to meet the clinical constraints with a 95 % confidence level. For both groups, the clinical plans were re-optimized, keeping the clinical objectives and constraints, but reducing robustness for the CTV objective (group A) to meet the probabilistic goal, or for the dose-limiting OAR objectives (group B) without exceeding the constraints. For the original and re-optimized plans, polynomial chaos expansion was applied to simulate 10,000 fractionated treatments, deriving probability distributions for relevant DVH parameters. Results: For group A, re-optimization resulted in a population median decrease of 8.2 (range: 0.4–20.8) Gy RBE in the total OAR-related clinical goal values. For group B, re-optimization resulted in a population median increase of 2.7 (range: 1.3–6.8) Gy RBE in the D_99.8%,CTV. The population median V_95%,CTV improved from 97.4 % to 99.1 %. Conclusion: We demonstrated that probabilistic evaluation guided IMPT planning enables either OAR sparing or target coverage enhancement.

Background and purpose: In online-adaptive proton therapy planning based on cone beam computed tomography (CBCT), CT number errors can pose challenges. We propose an approach for coping with CT number uncertainties by increasing range robustness settings (RRS) in online-adaptive planning. This was compared to our trigger-based offline (TB-Offline) adaptive approach, and to daily replanning using in-room CT-on-rails (CTOR). Material and methods: For 23 head-and-neck cancer patients, a CTOR and CBCT were acquired in a single fraction. CTOR contours were copied rigidly onto the CBCT. CBCT-based plans were generated with 3, 6, 8, 10, and 12 % RRS, each with 1 mm setup-RS, followed by a forward dose calculation on the reference CTOR. This was compared to dose distributions from our TB-Offline approach (3 mm/3% SRS/RRS), also recomputed on the CTOR. Coverage (voxelwise-minimum) of the primary clinical target volume (CTV₇₀₀₀) and elective lymph nodes (CTV₅₄₂₅) and grade ≥ II normal tissue complication probabilities were compared between strategies. Results: When going from RRS = 3 % to RRS = 10 %, the population 90th percentiles of CTV₅₄₂₅ V_94% improved from 89.6 % to 96.4 %, and CTV₇₀₀₀ V_94% from 92.8 % to 96.4 %. Substantial coverage loss (V_94%<95 %) with CBCT-based online adaptive and RRS = 10 % was observed in 1/23 evaluated patients for CTV₇₀₀₀ and 2/23 for CTV₅₄₂₅. This was an improvement compared to 3/23 and 4/23 with TB-Offline. Moreover, for RRS = 10 % the average risk of xerostomia improved by 2.4 percentage point compared to TB-Offline. Conclusions: Robust optimization with increased range robustness settings effectively mitigated dose degradation from CT number errors in CBCT-based online-adaptive proton therapy.

Background and purpose: In the Netherlands, 2 protocols have been standardized for PT among the 3 proton centers: a robustness evaluation (RE) to ensure adequate CTV dose and a model-based selection (MBS) approach for IMPT patient-selection. This multi-institutional study investigates (i) inter-patient and inter-center variation of target dose from the RE protocol and (ii) the robustness of the MBS protocol against treatment errors for a cohort of head-and-neck cancer (HNC) patients treated in the 3 Dutch proton centers. Materials and methods: Clinical treatment plans of 100 HNC patients were evaluated. Polynomial Chaos Expansion (PCE) was used to perform a comprehensive robustness evaluation per plan, enabling the probabilistic evaluation of 100,000 complete fractionated treatments. PCE allowed to derive scenario distributions of clinically relevant dosimetric parameters to assess CTV dose (D_99.8%/D_0.2%, based on a prior photon plan calibration) and tumour control probabilities (TCP) as well as the evaluation of the dose to OARs and normal tissue complication probabilities (NTCP) per center. Results: For the CTV_70.00, doses from the RE protocol were consistent with the clinical plan evaluation metrics used in the 3 centers. For the CTV_54.25, D_99.8% were consistent with the clinical plan evaluation metrics at center 1 and 2 while, for center 3, a reduction of 1 GyRBE was found on average. This difference did not impact modelled TCP at center 3. Differences between expected and nominal NTCP were below 0.3 percentage point for most patients. Conclusion: The standardization of the RE and MBS protocol lead to comparable results in terms of TCP and the NTCPs. Still, significant inter-patient and inter-center variation in dosimetric parameters remained due to clinical practice differences at each institution. The MBS approach is a robust protocol to qualify patients for PT.

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.

Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offline _TB re-planning) schedule, including extensive robustness analyses. Approach. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offline _TB re-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations. Main results. For 14/67 repeat-CTs, offline _TB re-planning resulted in <50% probability of D _98% ≥ 95% of the prescribed dose (D _pres) in one or both CTVs, which never happened with online re-optimization. With offline _TB re-planning, eight repeat-CTs had zero probability of obtaining D _98% ≥ 95%D _pres for CTV ₇₀₀₀, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p < 10 ⁻⁵ for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average. Significance. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

Objective. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient. Approach. We propose a deep learning probabilistic framework that generates deformation vector fields warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and ‘ground truth’ distributions of volume and center of mass changes. Results. With a DICE score of 0.86 ± 0.05 and a distance between prostate contours of 1.09 ± 0.93 mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM’s sampled movements match the range and frequency of clinically observed daily changes on repeat CTs. Significance. Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

Background and purpose: In the Netherlands, head-and-neck cancer (HNC) patients are referred for proton therapy (PT) through model-based selection (MBS). However, treatment errors may compromise adequate CTV dose. Our aims are: (i) to derive probabilistic plan evaluation metrics on the CTV consistent with clinical metrics; (ii) to evaluate plan consistency between photon (VMAT) and proton (IMPT) planning in terms of CTV dose iso-effectiveness and (iii) to assess the robustness of the OAR doses and of the risk toxicities involved in the MBS. Materials and methods: Sixty HNC plans (30 IMPT/30 VMAT) were included. A robustness evaluation with 100,000 treatment scenarios per plan was performed using Polynomial Chaos Expansion (PCE). PCE was applied to determine scenario distributions of clinically relevant dosimetric parameters, which were compared between the 2 modalities. Finally, PCE-based probabilistic dose parameters were derived and compared to clinical PTV-based photon and voxel-wise proton evaluation metrics. Results: Probabilistic dose to near-minimum volume v = 99.8% for the CTV correlated best with clinical PTV-D_98% and VWmin-D_98%,CTV doses for VMAT and IMPT respectively. IMPT showed slightly higher nominal CTV doses, with an average increase of 0.8 GyRBE in the median of the D_99.8%,CTV distribution. Most patients qualified for IMPT through the dysphagia grade II model, for which an average NTCP gain of 10.5 percentages points (%-point) was found. For all complications, uncertainties resulted in moderate NTCP spreads lower than 3 p.p. on average for both modalities. Conclusion: Despite the differences between photon and proton planning, the comparison between PTV-based VMAT and robust IMPT is consistent. Treatment errors had a moderate impact on NTCPs, showing that the nominal plans are a good estimator to qualify patients for PT.

Objective. The Dutch proton robustness evaluation protocol prescribes the dose of the clinical target volume (CTV) to the voxel-wise minimum (VWmin) dose of 28 scenarios. This results in a consistent but conservative near-minimum CTV dose (D98%,CTV). In this study, we analyzed (i) the correlation between VWmin/voxel-wise maximum (VWmax) metrics and actually delivered dose to the CTV and organs at risk (OARs) under the impact of treatment errors, and (ii) the performance of the protocol before and after its calibration with adequate prescription-dose levels.Approach. Twenty-one neuro-oncological patients were included. Polynomial chaos expansion was applied to perform a probabilistic robustness evaluation using 100,000 complete fractionated treatments per patient. Patient-specific scenario distributions of clinically relevant dosimetric parameters for the CTV and OARs were determined and compared to clinical VWmin and VWmax dose metrics for different scenario subsets used in the robustness evaluation protocol.Main results. The inclusion of more geometrical scenarios leads to a significant increase of the conservativism of the protocol in terms of clinical VWmin and VWmax values for the CTV and OARs. The protocol could be calibrated using VWmin dose evaluation levels of 93.0%-92.3%, depending on the scenario subset selected. Despite this calibration of the protocol, robustness recipes for proton therapy showed remaining differences and an increased sensitivity to geometrical random errors compared to photon-based margin recipes.Significance. The Dutch proton robustness evaluation protocol, combined with the photon-based margin recipe, could be calibrated with a VWmin evaluation dose level of 92.5%. However, it shows limitations in predicting robustness in dose, especially for the near-maximum dose metrics to OARs. Consistent robustness recipes could improve proton treatment planning to calibrate residual differences from photon-based assumptions.

Background and purpose: In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning. Materials and methods: For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR₃) and a trigger-based offline adaptive schedule for 3 mm SR (fSR₃OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT. Results: Compared to using fSR₃ and fSR₃OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR₃OfA resulted in significantly improved coverage compared to PL for selected patients. Conclusion: The proposed PL approach resulted in overall reduced NTCPs compared to fSR₃ and fSR₃OfA at limited cost in target coverage.

Background and purpose: Scenario-based robust optimization and evaluation are commonly used in proton therapy (PT) with pencil beam scanning (PBS) to ensure adequate dose to the clinical target volume (CTV). However, a statistically accurate assessment of the clinical application of this approach is lacking. In this study, we assess target dose in a clinical cohort of neuro-oncological patients, planned according to the DUPROTON robustness evaluation consensus, using polynomial chaos expansion (PCE). Materials and methods: A cohort of the first 27 neuro-oncological patients treated at HollandPTC was used, including realistic error distributions derived from geometrical and stopping-power prediction (SPP) errors. After validating the model, PCE-based robustness evaluations were performed by simulating 100.000 complete fractionated treatments per patient to obtain accurate statistics on clinically relevant dosimetric parameters and population-dose histograms. Results: Treatment plans that were robust according to clinical protocol and treatment plansin which robustness was sacrificed are easily identified. For robust treatment plans on average, a CTV dose of 3 percentage points (p.p.) more than prescribed was realized (range +2.7 p.p. to +3.5 p.p.) for 98% of the sampled fractionated treatments. For the entire patient cohort on average, a CTV dose of 0.1 p.p. less than prescribed was achieved (range −2.4 p.p. to +0.5 p.p.). For the 6 treatment plans in which robustness was clinically sacrificed, normalized CTV doses of 0.98, 0.94(7)¹, 0.94, 0.91, 0.90 and 0.89 were realized. The first of these was clinically borderline non-robust. Conclusion: The clinical robustness evaluation protocol is safe in terms of CTV dose as all plans that fulfilled the clinical robustness criteria were also robust in the PCE evaluation. Moreover, for plans that were non-robust in the PCE-based evaluation, CTV dose was also lower than prescribed in the clinical evaluation.

Breathing interplay effects in Intensity Modulated Proton Therapy (IMPT) arise from the interaction between target motion and the scanning beam. Assessing the detrimental effect of interplay and the clinical robustness of several mitigation techniques requires statistical evaluation procedures that take into account the variability of breathing during dose delivery. In this study, we present such a statistical method to model intra-fraction respiratory motion based on breathing signals and assess clinical relevant aspects related to the practical evaluation of interplay in IMPT such as how to model irregular breathing, how small breathing changes affect the final dose distribution, and what is the statistical power (number of different scenarios) required for trustworthy quantification of interplay effects. First, two data-driven methodologies to generate artificial patient-specific breathing signals are compared: a simple sinusoidal model, and a precise probabilistic deep learning model generating very realistic samples of patient breathing. Second, we investigate the highly fluctuating relationship between interplay doses and breathing parameters, showing that small changes in breathing period result in large local variations in the dose. Our results indicate that using a limited number of samples to calculate interplay statistics introduces a bigger error than using simple sinusoidal models based on patient parameters or disregarding breathing hysteresis during the evaluation. We illustrate the power of the presented statistical method by analyzing interplay robustness of 4DCT and Internal Target Volume (ITV) treatment plans for a 8 lung cancer patients, showing that, unlike 4DCT plans, even 33 fraction ITV plans systematically fail to fulfill robustness requirements.

As with all optical systems the calibration of wide-field ultraviolet (UV) systems includes three main areas: sensitivity, imaging quality, and imaging capability. The one thing that makes UV calibrations difficult is the need for working in vacuum substantially extending the required time and effort compared to visible systems. In theory a ray tracing and characterization of each individual component of the optical system (mirrors, windows, and grating) should provide the transmission efficiency of the combined system. However, potentially unknown effects (contamination, misalignment, and measurement errors) can make the final error too large and unacceptable for most applications. Therefore, it is desirable to test and measure the optical properties of the whole system in vacuum and compare the overall response to the response of a calibrated photon detector. A proper comparison then allows the quantification of individual sources of uncertainty and ensures that the whole instrument performance is within acceptable tolerances or pinpoints which parts fail to meet requirements. Based on the experience with the IMAGE Spectrographic Imager, the Wide-band Imaging Camera, and the ICON Far Ultraviolet instruments, we discuss the steps and procedures for the proper radiometric sensitivity and passband calibration, spot size, imaging distortions, flatfield, and field of view determination.