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.

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.

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.