Survey of patient-specific quality assurance practice for IMRT and VMAT

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy

OBJECTIVE

To quantify the difference between the (collapsed cone convolution) CCC algorithm and the (Monte Carlo) MC algorithm and remind that the planners should pay attention to some possible uncertainties of the two algorithms when employing the two algorithms.

METHODS

Thirty patients' cervical cancer VMAT plans were designed with a Pinnacle TPS (Philips) and divided equally into two groups: the simple group (SG, target volume was only the PTV) and the complex group (CG, target volume included the PTV and PGTV). The plans from the Pinnacle TPS were transferred to the Monaco TPS (Elekta). The plans' parameters all remained unchanged, and the dose was recalculated. Gamma passing rates (GPRs) obtained from dose distribution from Pinnacle TPS compared with that from Monaco TPS with SNC software based on three triaxial planes (transverse, sagittal and coronal). GPRs and DVH were used to quantify the difference between the CCC algorithm in pinnacle TPS and the MC algorithm in Monaco TPS.

RESULTS

Among the statistical dose indexes in DVHs from the Pinnacle and Monaco TPSs, there were 7(7/15) dose indexes difference with statistically significant differences in the SG, and 10(10/18) dose indexes difference with statistically significant differences in the CG. With 3%/3 mm criterion, the most (5/6) GPRs were greater than 95% from the SG and CG. But with 2%/2 mm criterion, the most (5/6) GPRs were less than 90% from the two groups. In addition, we found that GPRs were also related to the selected triaxial planes and the complexity of the plan (GPRs varied with the SG and CG).

CONCLUSIONS

Obvious difference between the CCC and MC algorithms from Pinnacle and Monaco TPS. DVH maybe better than 2D gamma analysis on quantifying difference of the CCC and MC algorithms. Some attention should be paid to the uncertainty of the TPS algorithm, especially when the indicator on the DVH is at the critical point of the threshold value, because the algorithm used may overestimate or underestimate the DVH indicator.

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.

Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process

PURPOSE

Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans.

MATERIALS AND METHODS

5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed.

RESULTS

The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR.

CONCLUSIONS

ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.

An effective and optimized patient-specific QA workload reduction for VMAT plans after MLC-modelling optimization

INTRODUCTION

Many complexity metrics characterize modulated plans. First, this study aimed at identify the optimal complexity metrics to reduce workload associated to patient-specific quality assurance (PSQA) for our equipment and processes. Second, it intended to optimize our MLC modelling to improve measurement and calculation agreement with expectation of further reducing PSQA workload.

METHODS

Correlation and sensitivity at specificity equals to 1 were evaluated for PSQA results and different complexity metrics. Thresholds to stop PSQA were determined. After validation of the optimal complexity metric and threshold for our equipment and process, the MLC modelling was reviewed with a recently published methodology. This method is based on measurements with a Farmer-type ionization chamber of synchronous and asynchronous sweeping gap plans. Effect on the PSQA results and the identified threshold was investigated.

RESULTS

In our center, the most appropriate complexity metric for reducing our PSQA workload was the Modulation Complexity Score for VMAT (MCSv). The optimization of the MLC modelling significantly reduced the number of controlled plans, specifically for one of our two Varian Clinac. Any plan with a MCSv >= 0.34 is treated without PSQA.

CONCLUSION

This study rationalized and reduced our PSQA workload by approximately 30%. It is a continuing work with new TPS, machine or PSQA equipment. It encourages centers to re-evaluate their MLC modelling as well as assess the benefit of complexity metrics to streamline their PSQA workflow. An easier access, at least for reporting, at best for optimizing plans, into the TPS would be beneficial for the community.

Cylindrical ionization chamber response in static and dynamic 6 and 15 MV photon beams

Purpose.To investigate the response of the CC13 ionization chamber under non-reference photon beam conditions, focusing on penumbra and build-up regions of static fields and on dynamic intensity-modulated beams.Methods. Measurements were performed in 6 MV 100 × 100, 20 × 100, and 20 × 20 mm²static fields. Monte Carlo calculations were performed for the static fields and for 6 and 15 MV dynamic beam sequences using a Varian multi-leaf collimator. The chamber was modelled using EGSnrc egs_chamber software. Conversion factors were calculated by relating the absorbed dose to air in the chamber air cavity to the absorbed dose to water. Correction and point-dose correction factors were calculated to quantify the conversion factor variations.Results. The correction factors for positions on the beam central axis and at the penumbra centre were 0.98-1.02 for all static fields and depths investigated. The largest corrections were obtained for chamber positions beyond penumbra centre in the off-axis direction. Point-dose correction factors were 0.54-0.71 at 100 mm depth and their magnitude increased with decreasing field size and measurement depth. Factors of 0.99-1.03 were obtained inside and near the integrated penumbra of the dynamic field at 100 mm depth, and of 0.92-0.94 beyond the integrated penumbra centre. The variations in the ionization chamber response across the integrated dynamic penumbra qualitatively followed the behaviour across penumbra of static fields.Conclusions. Without corrections, the CC13 chamber was of limited usefulness for profile measurements in 20-mm-wide fields. However, measurements in dynamic small irregular beam openings resembling the conditions of pre-treatment patient quality assurance were feasible. Uncorrected ionization chamber response could be applied for dose verification at 100 mm depth inside and close to large gradients of dynamically accumulating high- and low-dose regions assuming 3% tolerance between measured and calculated doses.

Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos

BACKGROUND

The clinical introduction of dedicated treatment units for online adaptive radiation therapy (OART) has led to widespread adoption of daily adaptive radiotherapy. OART allows for rapid generation of treatment plans using daily patient anatomy, potentially leading to reduction of treatment margins and increased normal tissue sparing. However, the OART workflow does not allow for measurement of patient-specific quality assurance (PSQA) during treatment delivery sessions and instead relies on secondary dose calculations for verification of adapted plans. It remains unknown if independent dose verification is a sufficient surrogate for PSQA measurements.

PURPOSE

To evaluate the plan quality of previously treated adaptive plans through multiple standard PSQA measurements.

METHODS

This IRB-approved retrospective study included sixteen patients previously treated with OART at our institution. PSQA measurements were performed for each patient's scheduled and adaptive plans: five adaptive plans were randomly selected to perform ion chamber measurements and two adaptive plans were randomly selected for ArcCHECK measurements. The same ArcCHECK 3D dose distribution was also sent to Mobius3D to evaluate the second-check dosimetry system.

RESULTS

All (n = 96) ion chamber measurements agreed with the planned dose within 3% with a mean of 1.4% (± 0.7%). All (n = 48) plans passed ArcCHECK measurements using a 95% gamma passing threshold and 3%/2 mm criteria with a mean of 99.1% (± 0.7%). All (n = 48) plans passed Mobius3D second-check performed with 95% gamma passing threshold and 5%/3 mm criteria with a mean of 99.0% (± 0.2%).

CONCLUSION

Plan measurement for PSQA may not be necessary for every online-adaptive treatment verification. We recommend the establishment of a periodic PSQA check to better understand trends in passing rates for delivered adaptive treatments.

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE

This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.

MATERIALS AND METHODS

Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.

RESULTS

The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.

CONCLUSIONS

Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

A Review of PRESAGE Radiochromic Polymer and the Compositions for Application in Radiotherapy Dosimetry

Recent advances in radiotherapy technology and techniques have allowed a highly conformal radiation to be delivered to the tumour target inside the body for cancer treatment. A three-dimensional (3D) dosimetry system is required to verify the accuracy of the complex treatment delivery. A 3D dosimeter based on the radiochromic response of a polymer towards ionising radiation has been introduced as the PRESAGE dosimeter. The polyurethane dosimeter matrix is combined with a leuco-dye and a free radical initiator, whose colour changes in proportion to the radiation dose. In the previous decade, PRESAGE gained improvement and enhancement as a 3D dosimeter. Notably, PRESAGE overcomes the limitations of its predecessors, the Fricke gel and the polymer gel dosimeters, which are challenging to fabricate and read out, sensitive to oxygen, and sensitive to diffusion. This article aims to review the characteristics of the radiochromic dosimeter and its clinical applications. The formulation of PRESAGE shows a delicate balance between the number of radical initiators, metal compounds, and catalysts to achieve stability, optimal sensitivity, and water equivalency. The applications of PRESAGE in advanced radiotherapy treatment verifications are also discussed.

Impact of stringent tolerance criteria on verification of absorbed dose distributions and evaluation through inhomogeneous media

Advances of radiation delivery devices have increased the complexity of the radiation oncology treatments. Herewith, outcome of the treatment, as well as patient safety, strongly depend on the consistency of absorbed dose delivery. Both can be ensured by comprehensive system of verification of calculated absorbed dose distributions. Standard method is evaluation of calculated absorbed dose distribution according to gamma method, using a 2-D detector and a homogeneous phantom, to obtain measured dose distribution. Purpose of this research was to investigate the influence of tolerance criteria on gamma passing rate. Additionally, the agreement in heterogeneous phantom was analysed. Absorbed dose calculations were performed using systems Monaco and XiO. Detector with 1020 ionization chambers in homogeneous phantom and semi-anthropomorphic phantom was used for measurements. Absorbed dose distributions of around 3500 patients were analysed using gamma method. In homogeneous phantom, average gamma passing rates were within tolerance for 3 %/2 mm. For measurements in heterogeneous media, the highest average gamma passing rate was obtained for small volumes of medium treatment complexity (??=93.84%), while large volumes of treatment with low complexity yielded the lowest gamma passing rates (??= 83.22%).