Purpose: The aim of this work was to evaluate the feasibility of cone-beam computed tomography (CBCT) and deformable image registration (DIR)-based ''dose of the day'' calculations for adaptive proton therapy. Methods: Intensity-modulated radiation therapy (IMRT) and proton therapy plans were designed for 3 head and neck patients that required replanning, and hence had a replan computed tomography (CT). Proton plans were generated for different beam arrangements and optimizations: intensity modulated proton therapy and single-field uniform dose. We used an in-house DIR software implemented at our institution to generate a deformed CT, by warping the planning CT onto the daily CBCT. This CBCT had a similar patient geometry to the replanned CT. Dose distributions on the replanned CT were considered the gold standard for ''dose of the day'' calculations, and were compared with doses on deformed CT (our method) and directly on the calibrated CBCT and rigidly aligned planning CT (alternative methods) in terms of dose difference (DD), by calculating the percentage of voxels whose DD was smaller than 2% of the prescribed dose (DD 2%-pp) and the root mean square of the DD distribution (DD RMS). Results: Using a deformed CT, the DD 2%-pp within the CBCT imaging volume was 93.2% 6 0.7% for IMRT, and 87% 6 3% for proton plans. In a region of higher dose gradient, we found that although DD 2%-pp was 94.3% 6 0.2% for IMRT, in proton plans, it dropped to 74% 6 4%. A larger number of treatment beams and single-field uniform dose optimization appear to make the proton plans less sensitive to DIR errors. For example, within the treated volume, the DD RMS was reduced from 2.6% 6 0.6% of the prescribed dose to 1.0% 6 1.3% of the prescribed dose when using single-field uniform dose optimization. Conclusions: Promising results were found for DIR-and CBCT-based proton dose calculations. Proton dose calculations were, however, more sensitive to registration errors than IMRT doses were, particularly in high dose gradient regions.

Purpose In pencil beam scanning proton therapy, target coverage is achieved by scanning the pencil beam laterally in the x‐ and y‐directions and delivering spots of dose to positions at a given radiological depth (layer). Dose is delivered to the spots on different layers by pencil beams of different energy until the entire volume has been irradiated. The aim of this study is to investigate the implementation of proton planning parameters (spot spacing, layer spacing and margins) in four commercial proton treatment planning systems (TPSs): Eclipse, Pinnacle3, RayStation and XiO. Materials and Methods Using identical beam data in each TPS, plans were created on uniform material synthetic phantoms with cubic targets. The following parameters were systematically varied in each TPS to observe their different implementations: spot spacing, layer spacing and margin. Additionally, plans were created in Eclipse to investigate the impact of these parameters on plan delivery and optimal values are suggested. Results It was found that all systems except Eclipse use a variable layer spacing per beam, based on the Bragg peak width of each energy layer. It is recommended that if this cannot be used, then a constant value of 5 mm will ensure good dose homogeneity. Only RayStation varies the spot spacing according to the variable spot size with depth. If a constant spot spacing is to be used, a value of 5 mm is recommended as a good compromise between dose homogeneity, plan robustness and planning time. It was found that both Pinnacle3 and RayStation position spots outside of the defined volume (target plus margin). Conclusions All four systems are capable of delivering uniform dose distributions to simple targets, but their implementation of the various planning parameters is different. In this paper comparisons are made between the four systems and recommendations are made as to the values that will provide the best compromise in dose homogeneity and planning time.

With the number of new proton centers increasing rapidly, there is a need for an assessment of the available proton treatment planning systems (TPSs). This study compares the dose distributions of complex meningioma plans produced by three proton TPSs: Eclipse, Pinnacle 3 , and XiO. All three systems were commissioned with the same beam data and, as best as possible, matched configuration settings. Proton treatment plans for ten patients were produced on each system with a pencil beam scanning, single-field uniform dose approach, using a fixed horizontal beamline. All 30 plans were subjected to identical dose constraints, both for the target coverage and organ at risk (OAR) sparing, with a consistent order of priority. Beam geometry, lateral field margins, and lateral spot resolutions were made consistent across all systems. Few statistically significant differences were found between the target coverage and OAR sparing of each system, with all optimizers managing to produce plans within clinical tolerances (D2 < 107% of prescribed dose, D5 < 105%, D95 > 95%, D99 > 90%, and OAR maximum doses) despite strict constraints and overlapping structures.

Purpose: We proposed two anatomical models for head and neck patients to predict anatomical changes during the course of radiotherapy. Methods: Deformable Image Registration was used to build two anatomical models: 1) The average model (AM) simulated systematic progression changes across the patient cohort; 2) The refined individual model (RIM) used a patient's CT images acquired during treatment to update the prediction for each individual patient. Planning CTs and weekly CTs were used from 20 nasopharynx patients. This dataset included 15 training patients and 5 test patients. For each test patient, a spot scanning proton plan was created. Models were evaluated using CT number difference, contours, proton spot location deviation and gamma index. Results: If no model was used, the CT number difference between the planning CT and the repeat CT at week 6 of treatment was on average 128.9 HU over the test population. This can be reduced to 115.5 HU using the AM, and to 110.5 HU using the RIM3 (RIM, updated at week 3). When the predicted contours from the models were used, the average mean surface distance of parotid glands can be reduced from 1.98 mm (no model) to 1.16 mm (AM) and 1.19 mm (RIM3) at week 6. Using proton spot range, the average anatomical uncertainty over the test population reduced from 4.47±1.23 mm (no model) to 2.41±1.12 mm (AM), and 1.89±0.96 mm (RIM3). Based on the gamma analysis, the average gamma index over the test patients was improved from 93.87±2.48 % (no model) to 96.16±1.84 % (RIM3) at week 6. Conclusions: The AM and the RIM both demonstrated the ability to predict anatomical changes during the treatment. The RIM can gradually refine the prediction of anatomical changes based on the AM. The proton beam spots provided an accurate and effective way for uncertainty evaluation.

Aims Twenty per cent of patients with non-small cell lung cancer present with stage III locally advanced disease. Precision radiotherapy with pencil beam scanning (PBS) protons may improve outcomes. However, stage III is a heterogeneous group and accounting for complex tumour motion is challenging. As yet, it remains unclear as to whom will benefit. In our retrospective planning study, we explored if patients with superior sulcus tumours (SSTs) are a select cohort who might benefit from this treatment. Materials and methods Patients with SSTs treated with radical radiotherapy using four-dimensional planning computed tomography between 2010 and 2015 were identified. Tumour motion was assessed and excluded if greater than 5 mm. Photon volumetric-modulated arc therapy (VMAT) and PBS proton single-field optimisation plans, with and without inhomogeneity corrections, were generated retrospectively. Robustness analysis was assessed for VMAT and PBS plans involving: (i) 5 mm geometric uncertainty, with an additional 3.5% range uncertainty for proton plans; (ii) verification plans at maximal inhalation and exhalation. Comparative dosimetric and robustness analyses were carried out. Results Ten patients were suitable. The mean clinical target volume D95 was 98.1% ± 0.4 (97.5–98.8) and 98.4% ± 0.2 (98.1–98.9) for PBS and VMAT plans, respectively. All normal tissue tolerances were achieved. The same four PBS and VMAT plans failed robustness assessment. Inhomogeneity corrections minimally impacted proton plan robustness and made it worse in one case. The most important factor affecting target coverage and robustness was the clinical target volume entering the spinal canal. Proton plans significantly reduced the mean lung dose (by 21.9%), lung V5, V10, V20 (by 47.9%, 36.4%, 12.1%, respectively), mean heart dose (by 21.4%) and thoracic vertebra dose (by 29.2%) ( P < 0.05). Conclusions In this planning study, robust PBS plans were achievable in carefully selected patients. Considerable dose reductions to the lung, heart and thoracic vertebra were possible without compromising target coverage. Sparing these lymphopenia-related organs may be particularly important in this era of immunotherapy.

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Objective: Develop an anatomical model based on the statistics of the population data and evaluate the model for anatomical robust optimisation in head and neck (H\&N) cancer proton therapy. Approach: Deformable Image Registration (DIR) was used to build the probability model (PM) that captured the major deformation from patient population data and quantified the probability of each deformation. A cohort of 20 nasopharynx patients was included in this retrospective study. Each patient had a planning CT and 6 weekly CTs during radiotherapy. We applied the model to 5 test patients. Each test patient used the remaining 19 training patients to build the PM and estimate the likelihood of a certain anatomical deformation to happen. For each test patient, a spot-scanning proton plan was created. The PM was evaluated using proton spot location deviation and dose distribution. Main results: Using the proton spot range, the PM can simulate small non-rigid variations in the first treatment week within 0.21 ±0.13 mm. For overall anatomical uncertainty prediction, the PM can reduce anatomical uncertainty from 4.47±1.23 mm (no model) to 1.49±1.08 mm at week 6. The 95% confidence interval (CI) of dose metric variations caused by actual anatomical deformations in the first week is -0.59 ± -0.31 % for low-risk CT D95, and 0.84±3.04 Gy for parotid Dmean. On the other hand, the 95% CI of dose metric variations simulated by the PM at the first week is -0.52 ± -0.34\% for low-risk CTV D95, and 0.58 ±2.22 Gy for parotid Dmean. Significance: The PM improves the estimation accuracy of anatomical uncertainty compared to the previous models and does not depend on the acquisition of the weekly CTs during the treatment. We provided a solution to quantify the probability of an anatomical deformation. The potential of the model for anatomical robust optimisation is discussed.

To assess a model to predict anatomical changes in head and neck cancer patients and explore the applicability of the model as a tool for adaptive replanning in intensity-modulated proton therapy (IMPT). Materials/Methods: 20 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT and weekly CTs during radiotherapy. To build the anatomical model, we deform the weekly CTs to planning CTs of our training population (n = 19) and obtain the average anatomical change per week. To predict a deformation for the remaining patient, the average deformation of the training population is applied to the patient's planning CT. The model is updated based on the patient's progression during treatment. K-fold cross validation (n = 5) was used to obtain a sample of 5 patients. For those 5 patients, we compare the accumulated dose of two adaptive IMPT strategies. 1) Predictive replan (PR): Replans were optimized on predicted images of weeks 3 and 5 and applied to week 3/4 and week 5/6 respectively. 2) Best-case replan (BcR): Adaptive replan on weekly CT is triggered when target coverage D95% criteria are not met and parotid gland mean dose deviation > 3 Gy (RBE). All plans (original plan, PR, BcR) were robustly optimized (+/-3 mm setup and +/-3.5% range uncertainty). Dosimetric goals: CTV D95(%) > 95% prescription dose in robust evaluation; maximum dose to spinal cord and brainstem < 45 Gy (RBE) and < 55 Gy (RBE), respectively. To ensure fairness of comparison, we kept dosimetric parameters between BcR and PR consistent. Results: Accumulated dose differences from two adaptive strategies (PR-BcR) of our 5 testing patients are shown (tab 1). We observe a maximum dose difference of 2.2% in the CTV D 95 , -1.35 Gy (RBE) in the brainstem D max , and -1.24 Gy (RBE) in the parotid D mean . The parentheses indicate the dose difference of the replans only (no accumulation). Conclusion: A predictive model was applied to the replanning process for adaptive proton therapy and compared to a best-case replan strategy. Our suggested PR produces clinically acceptable plans, comparable to the bestcase replan strategy, suggesting that the predictive models can be used for adaptive replanning. Application of predictive replanning provides the possibility to prepare adaptive plans in advance, streamlining the clinical workflow.Abstract 3173 − Table 1: Dose differences between predictive strategy and standard replan Accumulated dose difference PS-SR (replan dose difference) Id High-risk CTV Low-risk CTV Spinal cord Brainstem Parotid