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.

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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

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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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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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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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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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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