Proton therapy is affected by range uncertainty, which is partly caused by an ambiguous conversion from x-ray attenuation to proton stopping power. CT calibration curves, or Hounsfield look-up tables (HLUTs), are institution-specific and may be a source of systematic errors in treatment planning. A range probing method to verify, optimize and validate HLUTs for proton treatment is proposed. An initial HLUT was determined according to the stoichiometric approach. For HLUT validation, three types of animal tissue phantoms were prepared: a pig’s head, ‘thorax’ and femur. CT scans of the phantoms were taken and a structure, simulating a water slab, was added on the scan distal to the phantoms to mimic the detector used for integral depth-dose measurements. The CT scans were imported into the TPS to calculate individual pencil beams directed through the phantoms. The phantoms were positioned at the therapy system isocenter using x-ray imaging. Shoot-through pencil beams were delivered, and depth-dose profiles were measured using a multi-layer ionization chamber. Measured depth-dose curves were compared to the calculated curves and the range error per spot was determined. Based on the water equivalent path length (WEPL) of individual spot, a range error margin was defined. Ratios between measured error and theoretical margin were calculated per spot. The HLUT optimization was performed by identifying systematic shifts of the mean range error per phantom and minimizing the ratios between range errors and uncertainty margins. After optimization, the ratios of the actual range error and the uncertainty margin over the complete data set did not exceed 0.75 (1.5 SD), indicating that the actual errors are covered by the theoretical uncertainty recipe. The feasibility of using range probing to assess range errors was demonstrated. The theoretical uncertainty margins in the institution-specific setting potentially may be reduced by ~25%.

Thoracic tumours are increasingly considered indications for pencil beam scanned proton therapy (PBS-PT) treatments. Conservative robustness settings have been suggested due to potential range straggling effects caused by the lung micro-structure. Using proton radiography (PR) and a 4D porcine lung phantom, we experimentally assess range errors to be considered in robust treatment planning for thoracic indications. A human-chest-size 4D phantom hosting inflatable porcine lungs and corresponding 4D computed tomography (4DCT) were used. Five PR frames were planned to intersect the phantom at various positions. Integral depth-dose curves (IDDs) per proton spot were measured using a multi-layer ionisation chamber (MLIC). Each PR frame consisted of 81 spots with an assigned energy of 210 MeV (full width at half maximum (FWHM) 8.2 mm). Each frame was delivered five times while simultaneously acquiring the breathing signal of the 4D phantom, using an ANZAI load cell. The synchronised ANZAI and delivery log file information was used to retrospectively sort spots into their corresponding breathing phase. Based on this information, IDDs were simulated by the treatment planning system (TPS) Monte Carlo dose engine on a dose grid of 1 mm. In addition to the time-resolved TPS calculations on the 4DCT phases, IDDs were calculated on the average CT. Measured IDDs were compared with simulated ones, calculating the range error for each individual spot. In total, 2025 proton spots were individually measured and analysed. The range error of a specific spot is reported relative to its water equivalent path length (WEPL). The mean relative range error was 1.2% (1.5 SD 2.3 %) for the comparison with the time-resolved TPS calculations, and 1.0% (1.5 SD 2.2 %) when comparing to TPS calculations on the average CT. The determined mean relative range errors justify the use of 3% range uncertainty for robust treatment planning in a clinical setting for thoracic indications.

ESTRO 37 S533 0.7%(-12.9% to 20.2%) and 16.3% (-33.8% to 66.4%), respectively.The amplitude deviation was in average within 2 mm (figure 1).However, underestimations up to 6.3mm were observed.No correlations were found between acquisition parameters and volume or amplitude deviations. ConclusionOur data suggest that the expiration phase is the most accurate phase to define the tumour volume and should therefore be preferred for GTV delineation when using a mid-position, gating or tracking strategy.The large variation found among the institutions indicated that further improvements in 4DCT imaging are possible and local 4DCT QA could be improved.

Head and neck cancer (HNC) patients will be selected for proton therapy by the so-called "model-based approach", requiring a photon and proton plan comparison. So far, robust optimized proton plans were compared to non-robust optimized photon plans. To make a fair comparison between proton and photon plans we investigate the benefit of robust photon plan optimization in HNC. The secondary objective was to investigate the benefit of adaptive and 4D robust optimization. Ten HNC patients clinically treated with VMAT were included. All patients received a simultaneous integrated boost technique with 70 Gy (boost area) and 54.25 Gy (elective area) in 35 fractions. Furthermore, weekly rescan CT and daily cone-beam CT (CBCTs) scans were acquired. The non-robust optimized (i.e. PTV-based) VMAT plans were robust re-optimized (i.e. CTV-based) using worst-case robust objectives on the target volumes. The non-robust and robust plans were adapted after 15 fractions (3 weeks of treatment). In addition, the robust optimized plans were also adapted using 4D optimization using the first three rescan CTs or the first three rescan CTs and the first 15 CBCTs. For all planning strategies, the actual given dose was estimated by calculating the dose on 35 daily CBCT scans after online position correction, deforming the dose distributions to the reference planning CT and summing the doses of all fractions. Treatment plan quality was evaluated using dosimetric parameters and NTCP values using previously published models for tube feeding dependence, grade 2-4 dysphagia and xerostomia, 6 months after treatment. Robust optimized plans resulted in a lower actual given dose estimation for all organs at risk (p < 0.05) and improved coverage. The average NTCP values were lower for the robust optimized plans by 0.6% (p = 0.02) for tube feeding dependence, 2.1% (p = 0.01) for dysphagia and 3.1% (p < 0.01) for xerostomia. A similar improvement was observed when comparing adapted robust optimized plans to non-robustly optimized plans. Robust VMAT optimization in HNC patients resulted in lower normal tissue dose and NTCP values with similar target coverage compared to non-robust optimized VMAT plans in both an adaptive and non-adaptive setting. The addition of 4D-optimized plan adaptations using weekly CTs and/or daily CBCTs did not further improve the estimated actual given dose.Abstract 3742Mean dose (Gy)NTCP (%)Cricopharyngeal inletParotid ipsiParotid contraPCM InfPCM SupSupraglottic larynxTube FeedingDysphagiaXerostomiaNo plan adaptationNon-robust21.731.423.039.749.934.02.715.841.4Robust19.6*27.8*20.3*36.4*47.9*31.5*2.1*13.7*38.3*Plan adaptation after 15 fractionsNon-robust22.331.222.940.449.734.92.616.041.3Robust20.2*27.9*20.3*37.1*47.3*32.1*2.0*13.7*38.4*4D (CTs)20.4*28.0*20.5*37.0*47.9*31.9*2.1*13.8*38.6*4D (CTs + CBCTs)20.128.0*20.5*36.8*47.9*31.7*2.1*13.8*38.6**: paired t-test p < 0.05, comparing to the non-robust plan Open table in a new tab

Adaptive radiation therapy for head and neck cancer patients requires acquisition of many images, including planning (pCT) scans, CBCTs and rescans (rCT). These rCTs allow verification of the treatment plan on the current anatomy (e.g., by dose recalculation), and support the decision for plan adaptation. Re-planning is time-consuming, and is expected only to be required for a small proportion of cases. This work proposes a fast and efficient method to identify patients that qualify for re-planning. With such a method, full dose recalculations are only required for a limited number of rescans. Twenty-eight locally advanced head and neck cancer patients treated with intensity-modulated radiation therapy were included. For each patient, a pCT and rCT were acquired with an interval of 15-63 (mean 47) days. Clinical and planning target volumes (PTVs), spinal cord, eye structures and brain were contoured on the pCT and propagated to the rCT using a commercially available deformable image registration system. Dose distributions of the treatment plan were calculated on both the pCT and rCT. Ground truth re-planning decisions were made by applying constraints to the recalculated dose on the rCT, using the warped structures. Re-planning was indicated if spinal cord maximum dose (Dmax) > 50 Gy, brain Dmax > 60 Gy, eye structures Dmax > 60 Gy, or the PTV coverage was below 95% prescription value at D98%. For the therapeutic PTV, re-planning was also indicated if D1% was above 107% of prescription. As a surrogate for re-planning, a rigid translation was applied to the original dose distribution (on the pCT), to map it onto the rCT while keeping the isocenter localized correctly. The same set of constraints was then applied to this resulting dose, as if it represented the recalculated dose on the rCT. Of the 28 patients included in the study, nine required re-planning according to the criteria listed above, using dose recalculation on the rCT. Using the dose projection method, 11 patients were flagged as requiring re-planning, including all nine of the "true positive" patients. Thus, specificity is at 100%, and 89% of the true negatives were also correctly identified. We have shown that rigid dose projection onto rescan CTs is a fast and automated surrogate for dose recalculation, suitable for separating patients who may require re-planning from the majority who do not. This offers the potential for significant time saving, since full re-calculation and re-planning decisions need only be made on those patients flagged up by the fast system.