Repurposing DailyQA3 for an efficient and spot position sensitive daily quality assurance tool for proton therapy.

INTRODUCTION: Daily quality assurance is an integral part of a radiotherapy workflow to ensure the dose is delivered safely and accurately to the patient. It is performed before the first treatment of the day and needs to be time and cost efficient for a multiple gantries proton center. In this study, we introduced an efficient method to perform QA for output constancy, range verification, spot positioning accuracy and imaging and proton beam isocenter coincidence with DailyQA3. METHODS: A stepped acrylic block of specific dimensions is fabricated and placed on top of the DailyQA3 device. Treatment plans comprising of two different spread-out Bragg peaks and five individual spots of 1.0 MU each are designed to be delivered to the device. A mathematical framework to measure the 2D distance between the detectors and individual spot is introduced and play an important role in realizing the spot positioning and centering QA. Lastly, a 5 months trends of the QA for two gantries are presented. RESULTS: The outputs are monitored by two ion chambers in the DailyQA3 and a tolerance of ± 3 % $ \pm 3\% $ are used. The range of the SOBPs are monitored by the ratio of ion chamber signals and a tolerance of ± 1 mm $ \pm 1\ {\mathrm{mm}}$ is used. Four diodes at ± 10 cm $ \pm 10\ {\mathrm{cm}}$ from the central ion chambers are used for spot positioning QA, while the central ion chamber is used for imaging and proton beam isocenter coincidence QA. Using the framework, we determined the absolute signal threshold corresponding to the offset tolerance between the individual proton spot and the detector. A 1.5 mm $1.5\ {\mathrm{mm}}$ tolerances are used for both the positioning and centering QA. No violation of the tolerances is observed in the 5 months trends for both gantries. CONCLUSION: With the proposed approach, we can perform four QA items in the TG224 within 10 min.

First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre.

BACKGROUND AND PURPOSE: This work introduces the first assessment of CT calibration following the ESTRO's consensus guidelines and validating the HLUT through the irradiation of biological material. METHODS: Two electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider's and ESTRO's methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored. RESULTS: The results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider's and ESTRO's methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%. CONCLUSION: This is the first report on calibrating an HLUT following the ESTRO's guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO's guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

Effects of modern aesthetic dental fillings on proton therapy.

Background and purpose: High-density dental fillings pose a non-negligible impact on head and neck cancer treatment. For proton therapy, stopping power ratio (SPR) prediction will be significantly impaired by the associated image artifacts. Dose perturbation is also inevitable, compromising the treatment plan quality. While plenty of work has been done on metal or amalgam fillings, none has touched on composite resin (CR) and glass ionomer cement (GIC) which have seen an increasing usage. Hence, this work aims to provide a detailed characterisation of SPR and dose perturbation in proton therapy caused by CR and GIC. Materials and methods: Four types of fillings were used: CR, Fuji Bulk (FB), Fuji II (FII) and Fuji IX (FIX). The latter three belong to GIC category. Measured SPR were compared with SPR predicted using single-energy computed tomography (SECT) and dual-energy computed tomography (DECT). Dose perturbation of proton beams with lower- and higher-energy levels was also quantified using Gafchromic films. Results: The measured SPR for CR, FB, FII and FIX were 1.68, 1.77, 1.77 and 1.76, respectively. Overall, DECT could predict SPR better than SECT. The lowest percentage error achieved by DECT was 19.7 %, demonstrating the challenge in estimating SPR, even for fillings with relatively lower densities. For both proton beam energies and all four fillings of about 4.5 mm thickness, the maximum dose perturbation was 3 %. Conclusion: This study showed that dose perturbation by CR and GIC was comparatively small. We have measured and recommended the SPR values for overriding the fillings in TPS.

Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.

BACKGROUND: Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. METHODS: Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. RESULTS: 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2 mm (2%/1 mm) criterion. CONCLUSION: The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.

Implementing dispersion measurement as part of scanning proton therapy commissioning and quality assurance.

Introduction. Dispersion in an accelerator quantifies the deviation of the proton trajectory when there is a momentum deviation. We present for the first time a safe method of measuring dispersion in the clinic, using a scintillator detector and the momentum deviations within a spill. This is an important accelerator quantity as we found that this is the reason behind the large dose fluctuation in our absolute dosimetry measurement.Methods. Dispersions are measured for nine energies in a Hitachi ProBeat system at three locations (isocenter and at two profile monitors) and at two gantry angles (0 and 90 degrees) by first measuring the spot position and momentum drift within a spill. The spot position drift is measured by the XRV-4000 at the isocenter, and by the two profile monitors located at 0.57 and 2.27 m from the isocenter. The momentum drift is calculated from the intra-spill range drift which is measured using the Ranger accessory. The dispersion at isocenter and its gradient are calculated using the weighted least square regression on the measured dispersions at the three locations. A constraint is formulated on the dispersion and its gradient to ensure minimal intra-spill spot position deviation around the isocenter.Results. The measured intra-spill range and spot positional drift at isocenter are less than0.25mmand0.7mmrespectively. The momentum spread calculated from the range drift are less than 0.08%. The dispersion at the isocenter ranged from0.50to4.30mand the zero-crossing happens upstream of isocenter for all energies. 2 of the 9 energies (168.0 and 187.5 MeV) violated the constraint and has an intra-spill spot positional deviation greater than1.0within5cmfrom the isocenter.Conclusion. This measurement is recommended as part of commissioning and annual quality assurance for accelerator monitoring and to ensure intra-spill spot deviations remain low.

Predicting dice similarity coefficient of deformably registered contours using Siamese neural network.

Objective. Automatic deformable image registration (DIR) is a critical step in adaptive radiotherapy. Manually delineated organs-at-risk (OARs) contours on planning CT (pCT) scans are deformably registered onto daily cone-beam CT (CBCT) scans for delivered dose accumulation. However, evaluation of registered contours requires human assessment, which is time-consuming and subjects to high inter-observer variability. This work proposes a deep learning model that allows accurate prediction of Dice similarity coefficients (DSC) of registered contours in prostate radiotherapy.Approach. Our dataset comprises 20 prostate cancer patients with 37-39 daily CBCT scans each. The pCT scans and planning contours were deformably registered to each corresponding CBCT scan to generate virtual CT (vCT) scans and registered contours. The DSC score, which is a common contour-based validation metric for registration quality, between the registered and manual contours were computed. A Siamese neural network was trained on the vCT-CBCT image pairs to predict DSC. To assess the performance of the model, the root mean squared error (RMSE) between the actual and predicted DSC were computed.Main results. The model showed promising results for predicting DSC, giving RMSE of 0.070, 0.079 and 0.118 for rectum, prostate, and bladder respectively on the holdout test set. Clinically, a low RMSE implies that the predicted DSC can be reliably used to determine if further DIR assessment from physicians is required. Considering the event where a registered contour is classified as poor if its DSC is below 0.6 and good otherwise, the model achieves an accuracy of 92% for the rectum. A sensitivity of 0.97 suggests that the model can correctly identify 97% of poorly registered contours, allowing manual assessment of DIR to be triggered.Significance. We propose a neural network capable of accurately predicting DSC of deformably registered OAR contours, which can be used to evaluate eligibility for plan adaptation.

A measurement validation of improved plan deliverability with monitor unit objective tool for spine stereotactic ablative radiotherapy.

Spine stereotactic body radiation therapy (SBRT) uses high dose per fraction for palliative pain control. The treatment plans are often heavily modulated due to close proximity to spinal cord and this can lead to poor plan quality which are susceptible to dose delivery discrepancy. Therefore, we aim to assess the effectiveness of the monitor unit (MU) objective tool in Eclipse treatment planning systems in modulating the plan complexity to improve the plan quality in spine SBRT. Seven retrospective spine SBRT plans are re-optimized using the MU objective tool in Eclipse TPS v13.6 and were compared with the original plans. The dose metrics of the tumor PTV were compared using D1cc. D99%, D95%, D0.03cc, D0.1cc, D0.35cc and D1cc, and that of cord PRV were compared using D0.03cc, D0.1cc, D0.35cc. Four different plan complexities were also calculated for the original and re-optimized plans to quantify the impact of the tool on the modulation. Patient specific quality assurance measurements were performed with Stereophan and SRS MapCheck, and analyzed using the 1%/1-mm and 2%/2-mm criteria with gamma analysis. The dose metrics of the PTV and cord PRV of the re-optimized and original plans are similar and still meet the planning dose constraints. In particular, the PTV dose coverage has a small percentage difference of (0.15 ± 1.33)% and (0.01 ± 1.04)% for D99% and D95%, respectively. The 4 calculated plan complexity metrics consistently show that the re-optimized plans are quantitatively less complex than the original plan. The gamma passing rate of the re-optimized plans improved from (92.2 ± 2.0)% to (94.2 ± 1.6)% with the 1%/1-mm criterion, and (98.7 ± 1.0)% to (99.5 ± 0.3)% with the 2%/2-mm criterion. Overall, the re-optimized plans achieve at least a 10% MU reduction (11.7% to 24.6%). Our study shows that optimization with the MU objective tool can reduce plan complexity and improves dose delivery accuracy, while not compromising the dose distribution.

A review on fetal dose in Radiotherapy: A historical to contemporary perspective.

This paper aims to review on fetal dose in radiotherapy and extends and updates on a previous work1 to include proton therapy. Out-of-field doses, which are the doses received by regions outside of the treatment field, are unavoidable regardless of the treatment modalities used during radiotherapy. In the case of pregnant patients, fetal dose is a major concern as it has long been recognized that fetuses exposed to radiation have a higher probability of suffering from adverse effects such as anatomical malformations and even fetal death, especially when the 0.1Gy threshold is exceeded. In spite of the low occurrence of cancer during pregnancy, the radiotherapy team should be equipped with the necessary knowledge to deal with fetal dose. This is crucial so as to ensure that the fetus is adequately protected while not compromising the patient treatment outcomes. In this review paper, various aspects of fetal dose will be discussed ranging from biological, clinical to the physics aspects. Other than fetal dose resulting from conventional photon therapy, this paper will also extend the discussion to modern treatment modalities and techniques, namely proton therapy and image-guided radiotherapy, all of which have seen a significant increase in use in current radiotherapy. This review is expected to provide readers with a comprehensive understanding of fetal dose in radiotherapy, and to be fully aware of the steps to be taken in providing radiotherapy for pregnant patients.

A transit portal dosimetry method for respiratory gating quality assurance with a dynamic 3D printed tumor phantom.

BACKGROUNDS: Respiratory gating is one of the motion management techniques that is used to deliver radiation dose to a tumor at a specific position under free breathing. However, due to the dynamic feedback process of this approach, regular equipment quality assurance (QA) and patient-specific QA checks need to be performed. This work proposes a new QA methodology using electronic portal imaging detector (EPID) to determine the target localization accuracy of phase gating. METHODS: QA tools comprising 3D printed spherical tumor phantoms, programmable stages, and an EPID detector are characterized and assembled. Algorithms for predicting portal dose (PD) through moving phantoms are developed and verified using gamma analysis for two spherical tumor phantoms (2 cm and 4 cm), two different 6 MV volumetric modulated arc therapy plans, and two different gating windows (30%-70% and 40%-60%). Comparison between the two gating windows is then performed using the Wilcoxon signed-rank test. An optimizer routine, which is used to determine the optimal window, based on maximal gamma passing rate (GPR), was applied to an actual breathing curve and breathing plan. This was done to ascertain if our method yielded a similar result with the actual gating window. RESULTS: High GPRs of more than 97% and 91% were observed when comparing the predicted PD with the measured PD in moving phantom at 2 mm/2% and 1 mm/1% levels, respectively. Analysis of gamma heatmaps shows an excellent agreement with the tumor phantom. The GPR of 40%-60% PD was significantly lower than that of the 30%-70% PD at the 1 mm/1% level (p = 0.0064). At the 2 mm/2% level, no significant differences were observed. The optimizer routine could accurately predict the center of the gating window to within a 10% range. CONCLUSION: We have successfully performed and verified a new method for QA with the use of a moving phantom with EPID for phase gating with real-time position management.

Quantifying Systematic RBE-Weighted Dose Uncertainty Arising from Multiple Variable RBE Models in Organ at Risk.

PURPOSE: Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. METHODS: Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming ( α ß ) x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. RESULTS: The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique. CONCLUSIONS: Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different ( α ß ) x revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.

Prediction of portal dosimetry quality assurance results using log files-derived errors and machine learning techniques.

Objective: This work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime. Methods: 578 VMAT trajectory log files selected from prostate, lung and spine SBRT were used in this work. Four machine learning models were explored to identify the best performing regression model for predicting gamma passing rate within each sub-site and the entire unstratified data. Predictors used in these models comprised of hand-crafted log file-derived features as well as modulation complexity score. Cross validation was used to evaluate the model performance in terms of R2 and RMSE. Result: Using gamma passing rate of 1%/1mm criteria and entire dataset, LASSO regression has a R2 of 0.121 ± 0.005 and RMSE of 4.794 ± 0.013%, SVM regression has a R2 of 0.605 ± 0.036 and RMSE of 3.210 ± 0.145%, Random Forest regression has a R2 of 0.940 ± 0.019 and RMSE of 1.233 ± 0.197%. XGBoost regression has the best performance with a R2 and RMSE value of 0.981 ± 0.015 and 0.652 ± 0.276%, respectively. Conclusion: Log file-derived features can predict gamma passing rate of portal dosimetry with an average error of less than 2% using the 1%/1mm criteria. This model can potentially be applied to predict the patient specific QA results for every treatment fraction.

Standardizing Monte Carlo simulation parameters for a reproducible dose-averaged linear energy transfer.

OBJECTIVE: Dose-averaged linear energy transfer (LETD) is one of the factors which determines relative biological effectiveness (RBE) for treatment planning in proton therapy. It is usually determined from Monte Carlo (MC) simulation. However, no standard simulation protocols were established for sampling of LETD. Simulation parameters like maximum step length and range cut will affect secondary electrons production and have an impact on the accuracy of dose distribution and LETD. We aim to show how different combinations of step length and range cut in GEANT4 will affect the result in sampling of LETD using different MC scoring methods. METHODS: In this work, different step length and range cut value in a clinically relevant voxel geometry were used for comparison. Different LETD scoring methods were established and the concept of covariance between energy deposition per step and step length is used to explain the differences between them. RESULTS: We recommend a maximum step length of 0.05 mm and a range cut of 0.01 mm in MC simulation as this yields the most consistent LETD value across different scoring methods. Different LETD scoring methods are also compared and variation up to 200% can be observed at the plateau of 80 MeV proton beam. Scoring Method one has one of the lowest percentage differences compared across all simulation parameters. CONCLUSION: We have determined a set of maximum step length and range cut parameters to be used for LETD scoring in a 1 mm voxelized geometry. LETD scoring method should also be clearly defined and standardized to facilitate cross-institutional studies. ADVANCES IN KNOWLEDGE: Establishing a standard simulation protocol for sampling LETD would reduce the discrepancy when comparing data across different centres, and this can improve the calculation for RBE.

A depth dose study between AAA and AXB algorithm against Monte Carlo simulation using AIP CT of a 4D dataset from a moving phantom.

AIM: To identifying depth dose differences between the two versions of the algorithms using AIP CT of a 4D dataset. BACKGROUND: Motion due to respiration may challenge dose prediction of dose calculation algorithms during treatment planning. MATERIALS AND METHODS: The two versions of depth dose calculation algorithms, namely, Anisotropic Analytical Algorithm (AAA) version 10.0 (AAAv10.0), AAA version 13.6 (AAAv13.6) and Acuros XB dose calculation (AXB) algorithm version 10.0 (AXBv10.0), AXB version 13.6 (AXBv13.6), were compared against a full MC simulated 6X photon beam using QUASAR respiratory motion phantom with a moving chest wall. To simulate the moving chest wall, a 4 cm thick wax mould was attached to the lung insert of the phantom. Depth doses along the central axis were compared in the anterior and lateral beam direction for field sizes 2 × 2 cm2, 4 × 4 cm2 and 10 × 10 cm2. RESULTS: For the lateral beam direction, the moving chest wall highlighted differences of up to 105% for AAAv10.0 and 40% for AXBv10.0 from MC calculations in the surface and buildup doses. AAAv13.6 and AXBv13.6 agrees with MC predictions to within 10% at similar depth. For anterior beam doses, dose differences predicted for both versions of AAA and AXB algorithm were within 7% and results were consistent with static heterogeneous studies. CONCLUSIONS: The presence of the moving chest wall was capable of identifying depth dose differences between the two versions of the algorithms. These differences could not be identified in the static chest wall as shown in the anterior beam depth dose calculations.