Background: The steep radiation dose gradients in cervical cancer brachytherapy (BT) necessitate a thorough understanding of the behavior of afterloader source cables or needles in the curved channels of (patient-tailored) applicators. Purpose: The purpose of this study is to develop and validate computer models to simulate: (1) BT source positions, and (2) insertion forces of needles in curved applicator channels. The methodology presented can be used to improve the knowledge of instrument behavior in current applicators and aid the development of novel (3D-printed) BT applicators. Methods: For the computer models, BT instruments were discretized in finite elements. Simulations were performed in SPACAR by formulating nodal contact force and motion input models and specifying the instruments’ kinematic and dynamic properties. To evaluate the source cable model, simulated source paths in ring applicators were compared with manufacturer-measured source paths. The impact of discrepancies on the dosimetry was estimated for standard plans. To validate needle models, simulated needle insertion forces in curved channels with varying curvature, torsion, and clearance, were compared with force measurements in dedicated 3D-printed templates. Results: Comparison of simulated with manufacturer-measured source positions showed 0.5–1.2 mm median and <2.0 mm maximum differences, in all but one applicator geometry. The resulting maximum relative dose differences at the lateral surface and at 5 mm depth were 5.5% and 4.7%, respectively. Simulated insertion forces for BT needles in curved channels accurately resembled the forces experimentally obtained by including experimental uncertainties in the simulation. Conclusion: The models developed can accurately predict source positions and insertion forces in BT applicators. Insights from these models can aid novel applicator design with improved motion and force transmission of BT instruments, and contribute to the estimation of overall treatment precision. The methodology presented can be extended to study other applicator geometries, flexible instruments, and afterloading systems.

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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.

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Introduction
In head-and-neck IMPT, trigger-based offline plan adaptation (Offline trigger-based) is often used. Our goal was to compare this to four alternative adaptive strategies for dosimetry, workload and treatment time, considering also foreseen further technological advancements, including anticipated automation.

Materials and methods
Alternative strategies included weekly offline re-planning (Offline weekly), daily plan selection from a library (Library static and Library progressive) and a fast, approximate daily online re-optimization approach (Online re-opt). Impact on CTV coverage and NTCPs was assessed by simulations based on repeat-CTs from 15 patients. Full daily re-planning was used as dosimetric benchmark. Increases in workload and treatment time were estimated.

Results
Both for coverage and NTCPs, fast Online re-opt performed as well as full re-planning. Compared to current practice, Online re-opt showed enhanced probabilities for high coverage, and resulted in reductions in grade ≥ II NTCPs of 4.6 ± 1.7 %-point for xerostomia and 4.2 ± 2.3 %-point for dysphagia. Offline weekly and library strategies did not show coverage enhancements and resulted in smaller NTCP improvements. Further automation can largely limit workload and treatment time increases. With anticipated further automation, adaptation-related workload of Offline weekly, Library static, Library progressive, and Online re-opt was expected to increase by 3, 8, 21, and 66 h for 35 fraction treatment courses compared to Offline trigger-based. The corresponding adaptation-related prolonged treatment times were estimated to be 0, 4, 6, and 29 min/fraction.

Conclusion
Online adaptive strategies could approach dosimetric quality of full re-planning at the cost of additional workload and prolonged treatment time compared to the current offline adaptive strategy. Automation needs to play a key role in making more complex adaptive approaches feasible.@en

BACKGROUND AND PURPOSE: Although MRI-based image guided adaptive brachytherapy (IGABT) for locally advanced cervical cancer (LACC) has resulted in favorable outcomes, it can be logistically complex and time consuming compared to 2D image-based brachytherapy, and both physically and emotionally intensive for patients. This prospective study aims to perform time-action and patient experience analyses during IGABT to guide further improvements. MATERIALS AND METHODS: LACC patients treated with IGABT were included for the time-action (56 patients) and patient experience (29 patients) analyses. Times per treatment step were reported on a standardized form. For the patient experience analysis, a baseline health status was established with the EQ-5D-5L questionnaire and the perceived pain, anxiety and duration for each treatment step were assessed with the NRS-11. RESULTS: The median total procedure time from arrival until discharge was 530 (IQR: 480–565) minutes. Treatment planning (delineation, reconstruction, optimization) required the most time and took 175 (IQR: 145–195) minutes. Highest perceived pain was reported during applicator removal and treatment planning, anxiety during applicator removal, and duration during image acquisition and treatment planning. Perceived pain, anxiety and duration were correlated. Higher pre-treatment pain and anxiety scores were associated with higher perceived pain, anxiety and duration. CONCLUSION: This study highlights the complexity, duration and impact on patient experience of the current IGABT workflow. Patient reported pre-treatment pain and anxiety can help identify patients that may benefit from additional support. Research and implementation of measures aiming at shortening the overall procedure duration, which may include logistical, staffing and technological aspects, should be prioritized.

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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.

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Purpose: To develop and evaluate a fast, automated multi-criterial treatment planning approach for adaptive high-dose-rate (HDR) intracavitary + interstitial brachytherapy (BT) for locally advanced cervical cancer. Methods and materials: Twenty-two previously delivered single fraction MRI-based HDR treatment plans (SF_clin) were used to guide training of our in-house system for multi-criterial autoplanning, aiming for an autoplan quality superior to the training plans, while respecting the clinically desired “pear-shaped” dose distribution. Next, the configured algorithm was used to automatically generate treatment plans for 63 other fractions (SF_auto). The SF_auto plans were compared to the corresponding SF_clin plans in blind pairwise comparisons by an expert clinician. Then, the effect of adaptive autoplanning on total treatment (TT) plans (external beam + 3 BT fractions) was evaluated for 16 patients by simulating the clinically applied adaptive strategy to generate TT_auto plans and compare them with the corresponding clinical treatments (TT_clin). Results: In the blind comparisons, all SF_auto plans were considered clinically acceptable. In 62/63 comparisons, SF_auto plans were considered at least as good as, or better than the corresponding SF_clin. The average optimization time for autoplanning was 20.5 ± 19.2 s (range 4.4–106.4 s) per plan. In 14 of 16 TT_auto plans, the desired total dose of 90 Gy (EQD₂) was obtained, compared to only 9 in the corresponding TT_clin, while autoplanning also decreased bladder and rectum doses. Conclusions: Fast, fully-automated multi-criterial treatment planning for adaptive HDR-BT for locally advanced cervical cancer is feasible. Autoplans were superior to corresponding clinical plans.

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Segmentation can degrade a high-quality dose distribution obtained by fluence map optimisation (FMO). A novel algorithm is proposed for generation of MLC segments to deliver an FMO plan with step-and-shoot IMRT while minimising quality loss. All beams are considered simultaneously while generating MLC segments for reproducing the 3D FMO dose distribution. Segment generation is only steered by the 3D FMO dose distribution, i.e. underlying FMO fluence profiles are not considered. The algorithm features prioritised generation of segments, focusing on accurate reproduction of clinical objectives with the highest priorities. The performance of the segmentation algorithm was evaluated for 20 prostate patients, 15 head-and-neck patients, and 12 liver patients. FMO dose distributions were generated by automated multi-criteria treatment planning (Pareto-optimal plans) and subsequently segmented using the proposed method. Various segmentation strategies were investigated regarding prioritisation of objectives and limitation of the number of segments. Segmented plans were dosimetrically similar to FMO plans and for all patients a clinically acceptable segmented plan could be generated. Substantial differences between FMO and segmented fluence profiles were observed. Avoidance of the usual reconstruction of 2D FMO fluence profiles for segment generation, and instead simultaneously generating segments for all beams to directly reproduce the 3D FMO dose distribution is a likely explanation for the obtained results. For the strategies of limiting the number of segments large reductions in number of segments were observed with minimal impact on plan quality.

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We developed a fast and fully-automated, multi-criteria treatment planning workflow for high dose rate brachytherapy (HDR-BT). In this workflow, the patient-CT with catheter reconstructions and dwell positions are imported from the clinical TPS into a novel system for automated dwell time optimisation. The optimised dwell times are then imported into the clinical TPS. The aims of automation were (1) planner-independent, enhanced plan quality, (2) short optimisation times. Our in-house developed system for fully automated, multi-criteria external beam radiotherapy (EBRT) treatment planning (Erasmus-iCycle) was adapted for optimisation of HDR-BT dose distributions. The investigations were performed with planning CT scans with catheter reconstructions and delineations of twenty-five low- A nd intermediate-risk prostate cancer patients who were previously treated in our center with 4 × 9.5 Gy HDR-BT. Automatically generated plans (autoplans) were compared to the corresponding clinical plans. All evaluations were performed in the clinical TPS. The requested 95% tumour coverage was obtained for all autoplans, while this was only observed in 23/25 clinical plans. All autoplans showed a consistent reduction of the D1% for the highest prioritised OAR, the urethra. The average and maximum reductions were 6.3%-point and 12.1%-point of the prescribed dose, respectively. In addition, conformality of the autoplans was higher. The autoplans had slightly smaller delivery times. Autoplanning took on average 4.6 s, including computation of the dose kernels.

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In radiation therapy treatment planning, generating a treatment plan is a multi-objective optimisation problem. The decision-making strategy is uniform for each group of cancer patients, e.g. prostate cancer, and can thus be automated. Predefined priorities and aspiration levels are assigned to each objective, and the strategy is to attain these levels in order of priority. Therefore, a straightforward lexicographic approach is sequential ϵ-constraint programming where objectives are sequentially optimised and constrained according to predefined rules, mimicking human decision-making. The clinically applied 2-phase ϵ-constraint (2pϵc) method captures this approach and generates clinically acceptable treatment plans. However, the number of optimisation problems to be solved for the 2pϵc method, and hence the computation time, scales linearly with the number of objectives. To improve the daily planning workload and to further enhance radiation therapy, it is extremely important to reduce this time. Therefore, we developed the lexicographic reference point method (LRPM), a lexicographic extension of the reference point method, for generating a treatment plan by solving a single optimisation problem. The LRPM processes multiple a priori defined reference points into modified partial achievement functions. In addition, a priori bounds on a subset of the partial trade-offs can be imposed using a weighted sum component. The LRPM was validated for 30 randomly selected prostate cancer patients. While the treatment plans generated using the LRPM were of similar clinical quality to those generated using the 2pϵc method, the LRPM decreased the average computation time from 12.4 to 1.2 minutes, a speed-up factor of 10.

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Previously, we have proposed Erasmus-iCycle, an algorithm for fully automated IMRT plan generation based on prioritised (lexicographic) multi-objective optimisation with the 2-phase -constraint (2pc) method. For each patient, the output of Erasmus-iCycle is a clinically favourable, Pareto optimal plan. The 2pc method uses a list of objective functions that are consecutively optimised, following a strict, user-defined prioritisation. The novel lexicographic reference point method (LRPM) is capable of solving multi-objective problems in a single optimisation, using a fuzzy prioritisation of the objectives. Trade-offs are made globally, aiming for large favourable gains for lower prioritised objectives at the cost of only slight degradations for higher prioritised objectives, or vice versa. In this study, the LRPM is validated for 15 head and neck cancer patients receiving bilateral neck irradiation. The generated plans using the LRPM are compared with the plans resulting from the 2pc method. Both methods were capable of automatically generating clinically relevant treatment plans for all patients. For some patients, the LRPM allowed large favourable gains in some treatment plan objectives at the cost of only small degradations for the others. Moreover, because of the applied single optimisation instead of multiple optimisations, the LRPM reduced the average computation time from 209.2 to 9.5 min, a speed-up factor of 22 relative to the 2pc method.

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Purpose To quantify the impact of the degree of robustness against setup errors and range errors on organ-at-risk (OAR) dose and normal tissue complication probabilities (NTCPs) in intensity-modulated proton therapy for oropharyngeal cancer patients. Material and methods For 20 oropharyngeal cases (10 unilateral and 10 bilateral), robust treatment plans were generated using ‘minimax’ worst-case optimization. We varied the robustness against setup errors (‘setup robustness’) from 1 to 7 mm and the robustness against range errors (‘range robustness’) from 1% to 7% (+1 mm). We evaluated OAR doses and NTCP-values for xerostomia, dysphagia and larynx edema. Results Varying the degree of setup robustness was found to have a considerably larger impact than varying the range robustness. Increasing setup robustness from 1 mm to 3, 5, and 7 mm resulted in average NTCP-values to increase by 1.9, 4.4 and 7.5 percentage point, whereas they increased by only 0.4, 0.8 and 1.2 percentage point when increasing range robustness from 1% to 3%, 5% and 7%. The degree of setup robustness was observed to have a clinically significant impact in bilateral cases in particular. Conclusions For oropharyngeal cancer patients, minimizing setup errors should be given a higher priority than minimizing range errors.

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Purpose We aimed to derive a "robustness recipe" giving the range robustness (RR) and setup robustness (SR) settings (ie, the error values) that ensure adequate clinical target volume (CTV) coverage in oropharyngeal cancer patients for given Gaussian distributions of systematic setup, random setup, and range errors (characterized by standard deviations of Σ, σ, and ρ, respectively) when used in minimax worst-case robust intensity modulated proton therapy (IMPT) optimization. Methods and Materials For the analysis, contoured computed tomography (CT) scans of 9 unilateral and 9 bilateral patients were used. An IMPT plan was considered robust if, for at least 98% of the simulated fractionated treatments, 98% of the CTV received 95% or more of the prescribed dose. For fast assessment of the CTV coverage for given error distributions (ie, different values of Σ, σ, and ρ), polynomial chaos methods were used. Separate recipes were derived for the unilateral and bilateral cases using one patient from each group, and all 18 patients were included in the validation of the recipes. Results Treatment plans for bilateral cases are intrinsically more robust than those for unilateral cases. The required RR only depends on the ρ, and SR can be fitted by second-order polynomials in Σ and σ. The formulas for the derived robustness recipes are as follows: Unilateral patients need SR = -0.15Σ² + 0.27σ² + 1.85Σ - 0.06σ + 1.22 and RR=3% for ρ = 1% and ρ = 2%; bilateral patients need SR = -0.07Σ² + 0.19σ² + 1.34Σ - 0.07σ + 1.17 and RR=3% and 4% for ρ = 1% and 2%, respectively. For the recipe validation, 2 plans were generated for each of the 18 patients corresponding to Σ = σ = 1.5 mm and ρ = 0% and 2%. Thirty-four plans had adequate CTV coverage in 98% or more of the simulated fractionated treatments; the remaining 2 had adequate coverage in 97.8% and 97.9%. Conclusions Robustness recipes were derived that can be used in minimax robust optimization of IMPT treatment plans to ensure adequate CTV coverage for oropharyngeal cancer patients.

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