Patient Specific QA Research Articles

PO-1617 Multicentric evaluation of a machine learning model to streamline the RT patient-specific QA process

PO-1617 Multicentric evaluation of a machine learning model to streamline the RT patient-specific QA process

The effect of dose gradients on gamma comparison insensitivity in patient specific QA comparisons.

While many have speculated on the reasons for gamma comparison insensitivity for patient-specific quality assurance analysis, the true reasons for insensitivity have not yet been elucidated. Failing to understand the reasons for this technique's insensitivity limits our ability to either improve the gamma metric to increase sensitivity of the comparison or the capacity to develop new comparison techniques that circumvent the limitations of the gamma comparison. To understand the underlying cause(s) for gamma comparison insensitivity and determine if simple plan characteristics can quantitatively predict for gamma comparison sensitivity. Known MLC and MU errors of varying magnitudes were induced on simple test fields to preliminarily investigate where gamma failures first begin to appear as error magnitude is increased. Gamma value maps between error-induced plan calculations and error-free plan calculations were created for 20 IMRT and 20 VMAT cases, each on three different detector geometries-ArcCHECK, MapCHECK, and Delta4. Gamma value maps were qualitatively compared to dose-gradient maps, and quantitative comparisons were performed between various plan descriptors and the computed gamma sensitivity for five different classes of induced errors were utilized to determine if any plan descriptor could predict the gamma sensitivity on a case-by-case basis. All comparisons were performed in a calculation-only scenario to remove uncertainties introduced by comparisons made with real patient specific QA measurements. Gamma value maps with increasing induced error magnitude illustrated that gamma comparisons fail first in high-dose, low-gradient regions of the field. Conversely, in areas of high gradient, gamma values typically remain low, even in the presence of large errors, regardless of detector geometry and gamma normalization setting. Thus, the complex, and often overlapping, high dose gradients in plans appear to be a limiting factor in gamma comparison sensitivity as the number of points along these gradients may often outnumber the points available for failing the comparison in lower gradient regions of the field. None of the simple plan descriptors studied were able to quantitively predict gamma comparison sensitivity, suggesting that quantitatively predicting the sensitivity of gamma comparisons on a case-by-case basis may require a combination of multiple factors or metrics not studied here. Simple plan descriptors and the number of points in high-dose, low-gradient regions of the field did not quantitively predict for gamma comparison sensitivity. However, it is clear from gradient and gamma value maps that gamma comparisons fail first in high-dose, low-gradient regions of the field in the presence of known induced errors, which we have shown to be independent of detector geometry and gamma comparison normalization setting. Gamma comparison sensitivity is thus limited by the ever-increasing complexity of plans and is particularly important to consider as treatment volumes become smaller and the complexity of overlapping plan gradients increases. This suggests that new methods for patient-specific QA comparisons are required to circumvent this limitation.

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

IntroductionMany 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. MethodsCorrelation 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. ResultsIn 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. ConclusionThis 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.

The use of a mini-ridge filter with cyclotron-based pencil beam scanning proton therapy.

Pencil beam scanning (PBS) proton therapy allows for far superior dose conformality compared with passive scattering techniques. However, one drawback of PBS is that the beam delivery time can be long, particularly when treating superficial disease. Minimizing beam delivery time is important for patient comfort and precision of treatment delivery. Mini-ridge filters (MRF) have been shown to reduce beam delivery time for synchrotron-based PBS. Given that cyclotron systems are widely used in proton therapy it is necessary to investigate the potential clinical benefit of mini-ridge filters in such systems. To demonstrate the clinical benefit of using a MRF to reduce beam delivery time for patients with large target volumes and superficial disease in cyclotron-based PBS proton therapy. A MRF beam model was generated by simulating the effect of a MRF on our clinical beam data assuming a fixed snout position relative to the isocenter. The beam model was validated with a series of measurements. The model was used to optimize treatment plans in a water phantom and on six patient DICOM datasets to further study the effect of the MRF and for comparison with physician-approved clinical treatment plans. Beam delivery time was measured for six plans with and without the MRF to demonstrate the reduction achievable. Plans with and without MRF were reviewed to confirm clinical acceptability by a radiation oncologist. Patient-specific QA measurements were carried out with a two-dimensional ionization chamber array detector for one representative patient's plan optimized with the MRF beam model. Results show good agreement between the simulated beam model and measurements with mean and maximum deviations of 0.06mm (0.45%) and 0.61mm (4.9%). The increase in Bragg peak width (FWHM) ranged from 2.7mm at 226MeV to 6.1mm at 70 MeV. The mean and maximum reduction in beam delivery time observed per field was 29.1s (32.2%) and 79.7s (55.3%). MRFs can be used to reduce treatment time in cyclotron-based PBS proton therapy without sacrificing plan quality. This is particularly beneficial for patients with large targets and superficial disease such as in breast cancer where treatment times are generally long, as well as patients treated with deep inspiration breath hold (DIBH).

Clinical experience on patient-specific quality assurance for CBCT-based online adaptive treatment plan.

Ethos CBCT-based adaptive radiotherapy (ART) system can generate an online adaptive plan by re-optimizing the initial reference plan based on the patient anatomy at the treatment. The optimization process is fully automated without any room for human intervention. Due to the change in anatomy, the ART plan can be significantly different from the initial plan in terms of plan parameters such as the aperture shapes and number of monitor units (MUs). In this study, we investigated the feasibility of using calculation-based patient specific QA for ART plans in conjunction with measurement-based and calculation-based QA for initial plans to establish an action level for the online ART patient-specific QA. A cohort of 98 cases treated on CBCT-based ART system were collected for this study. We performed measurement-based QA using ArcCheck and calculation-based QA using Mobius for both the initial plan and the ART plan for analysis. For online the ART plan, Mobius calculation was conducted prior to the delivery, while ArcCheck measurement was delivered on the same day after the treatment. We first investigated the modulation factors (MFs) and MU numbers of the initial plans and ART plans, respectively. The γ passing rates of initial and ART plan QA were analyzed. Then action limits were derived for QA calculation and measurement for both initial and online ART plans, respectively, from 30 randomly selected patient cases, and were evaluated using the other 68 patient cases. The difference in MF between initial plan and ART-plan was 12.9% ± 12.7% which demonstrates their significant difference in plan parameters. Based on the patient QA results, pre-treatment calculation and measurement results are generally well aligned with ArcCheck measurement results for online ART plans, illustrating their feasibility as an indicator of failure in online ART QA measurements. Furthermore, using 30 randomly selected patient cases, the γ analysis action limit derived for initial plans and ART plans are 89.6% and 90.4% in ArcCheck QA (2%/2mm)andare 92.4% and 93.6% in Mobius QA(3%/2mm),respectively.According to the calculated action limits, the ArcCheck measurements for all the initial and ART plans passed QA successfully while the Mobius calculation action limits flagged seven and four failure cases respectively for initial plans and ART plans, respectively. An ART plan can be substantially different from the initial plan, and therefore a separate session of ART plan QA is needed to ensure treatment safety and quality. The pre-treatment QA calculation via Mobius can serve as a reliable indicator of failure in online ART plan QA. However, given that Ethos ART system is still relatively new, ArcCheck measurement of initial plan is still in practice. It may be skipped as we gain more experience and have better understanding of the system.

Evaluation of the Quality and Comparison of Dosimetric Parameters of Plans for Craniospinal Irradiation Using VMAT Method at Varian TrueBeam and Varian Halcyon Accelerators

Purpose: To carry out a comparative analysis of the dosimetric characteristics of plans for craniospinal irradiation (CSI) performed by the VMAT technique on Varian TrueBeam and Halcyon electron accelerators.
 Material and methods: Comparison of dose distributions was carried out for a group of 5 patients with tumors of the central nervous system. Contouring and planning were in Eclipse version 15.6. The following dose characteristics were calculated and evaluated: homogeneity index (HI), conformity index (CI), target coverage (CO) and constraints (lenses, esophagus, lungs, heart, liver, bowel bag and kidneys). Patient-Specific QA was performed to verify the consistency of the dose distribution calculated in the planning system and obtained at the accelerators.
 Results: For all CSI plans the target coverage metric was D98>95 % and constraints for critical organs were obtained. Homogeneity index was HI<=0.12 for two accelerators. The mean value of the CI conformity index was 1.09 for the TrueBeam accelerator, and CI=1.12 for the Halcyon. Gamma analysis showed results within tolerance both plans at TrueBeam and Halcyon. Conclusions have been made about the advantages and disadvantages of CSI on TrueBeam and Halcyon electron accelerators.
 Conclusion: It is possible to treat patients with the CSI method on the Halcyon accelerator using the VMAT method with the same quality as on the TrueBeam accelerator.

A novel Monte Carlo (MC) dose model for small MLC fields of the cyberknife® M6TM radiosurgery system using the EGSnrc.

The multi-leaf collimator (MLC)-equipped CyberKnife® M6 radiosurgery system (CKM6) (Accuray Inc., Sunnyvale, CA) has been increasingly employed for stereotactic radiosurgery (SRS) to treat relatively small lesions. However, achieving an accurate dose distribution in such cases is usually challenging due to the combination of numerous small fields≤(30×30)mm2 . In this study, we developed a new Monte Carlo (MC) dose model for the CKM6 system using the EGSnrc to investigate dose variations in the small fields. The dose model was verified for the static MLC fields ranging from (53.8×53.9) to (7.6×7.7) mm2 at 800mm source to axis distance in a water phantom, based on the computed doses of Accuray Precision® (Accuray Inc.) treatment planning system (TPS). We achieved a statistical uncertainty of ≤4% by simulating 30-50 million incident particles/histories. Then, the treatment plans were created for the same fields in the TPS, and the corresponding measurements were performed with MapCHECK2 (Sun Nuclear Corporation), a standard device for patient-specific quality assurance (PSQA). Results of the MC simulations, TPS, and MapCHECK2 measurements were inter-compared. An overall difference in dosimetric parameters such as profiles, tissue maximum ratio (TMR), and output factors (OF) between the MC simulations and the TPS results was found ≤3% for (53.8×53.9-15.4×15.4) mm2 MLC fields, and it rose to 4.5% for the smallest (7.6mm×7.7mm) MLC field. The MapCHECK2 results showed a deviation ranging from -1.5% to + 4.5% compared to the TPS results, whereas the deviation was within±2.5% compared with the MC results. Overall, our MC dose model for the CKM6 system showed better agreement with measurements and it could serve as a secondary dose verification tool for the patient-specific QA in small fields.

A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network.

Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy. The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN). The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated. The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively. We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.

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

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. 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. 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. 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.

Characterization of a commercial EPID-based in-vivo dosimetry and its feasibility and implementation for treatment verification in Malaysia

Abstract Introduction: In vivo dosimetry verification is currently a necessity in radiotherapy centres in Europe countries as one of the tools for patient-specific QA, and now its demand is currently rising in developed countries, such as Malaysia. The aim of this study is to characterize commercial EPID-based dosimetry and its implementation for radiotherapy treatment verification in Malaysia. Materials and Methods: In this work, the sensitivity and performance of a commercially available in vivo dosimetry system, EPIgray® (DOSIsoft, Cachan, France), were qualitatively evaluated prior to its use at our centre. EPIgray response to dose linearity, field size, off-axis, position, and angle dependency tests were performed against TPS calculated dose for 6 MV and 10 MV photon beams. Relative deviations of the total dose were evaluated at isocentre and different depths in the water. EPIgray measured dose was validated by using IMRT and VMAT prostate plan. All calculation points were at the beam isocentre and at points suggested by TG-119 with accepted tolerance of ±10% dose threshold. Results: EPIgray reported good agreement for linearity, field size, off-axis, and position dependency with TPS dose, being within 5% tolerance for both energy ranges. The average deviation was less than ±2% and ±7% in 6 MV and 10 MV photon beams, respectively, for the angle dependency test. A clinical evaluation performed for the IMRT prostate plan gave average agreement within ±3% at the plan isocentre for both energies. While for the VMAT plan, 95% and 100% of all points created lie below ±5% for 6 MV and 10 MV photon beam energy, respectively. Conclusion: In summary, based on the results of preliminary characterization, EPID-based dosimetry is believed as an important tool and beneficial to be implemented for IMRT/VMAT plans verification in Malaysia, especially for in vivo verification, alongside existing pre-treatment verification.

Statistical process control to monitor use of a web-based autoplanning tool.

To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. For RayStation, the average mean percent dose differences across all contours were 0.65%±1.05%, -2.09%±0.56%, and 0.28%±0.98% and average control limit ranges were 1.89%±1.32%, 2.16%±1.31%, and 2.65%±1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62%±0.34%, 0.32%±0.23%, and -0.91%±0.98%, while average control limit ranges were 1.09%±0.77%, 3.69%±2.67%, 2.73%±1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202±0.019cm and MLC-TF value of 0.020±0.001 for Acuros and a DLG value of 0.135±0.031cm and MLC-TF value of 0.015±0.001 for AAA. Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.

Geometric Analysis and Dosimetric Evaluation of Various Deformable Image Registration (DIR) Algorithms for Abdominal Images with 3D Biomechanically Modelled Deformation

<h3>Purpose/Objective(s)</h3> The accuracy of DIR algorithms available on commercial radiotherapy planning platforms were examined and their clinical applicability geometrically and dosimetrically for abdominal sites were evaluated using biomechanically modelled deformation. <h3>Materials/Methods</h3> 5 pancreatic patients' planning CT were used in this study. Following the guideline of AAPM TG-132, a patient-specific QA workflow was developed and implemented to evaluate DIR for abdomen using digital phantoms, created with a commercial virtual phantom software (VPS). First, the planning CT images were deformed in superior-inferior direction to simulation respiratory motion in lung and abdomen, using the biomechanical model embedded in VPS. In addition, 5 mm motion in both anterior-posterior and lateral directions were added to region of interest (ROI), including stomach and duodenum, and small bowel. The added ROI deformations are consistent with the abdomen data observed in daily CBCT. The resulting 3D deformation vector field (DVF) and the corresponding deformed CT was then obtained, serving as ground truth. Second, the original planning CT and the deformed CT were imported to two commercial DIR systems (DS1 and DS2), to perform DIR with intensity-based algorithms, and derive DVFs. Quantitative differences between ground truth DVF and DS1 or DS2's DVFs, such as mean, max, and the distribution of DIR errors were tabulated. Then the original plan was re-calculated on the deformed CT, which was generated based on DS1 or DS2's DVF. The dose errors were then calculated on a pixel-to-pixel basis. The relationship between the DIR errors and the dose error ranges (25%-75% on the dose error distribution histogram) were investigated. As a pilot study, comprehensive reports were generated to show the DIR performances as patient specific QA records. <h3>Results</h3> The displacements on the ground truth DVF range from 0 to 37 mm. Geometric analysis showed DIR errors as tabulated below. Dosimetrically, as the DIR error increases, the dose error range also increase, for both DS1 and DS2. Dosimetric evaluation of the dose errors for ROIs were tabulated as below. <h3>Conclusion</h3> Preliminary evaluation of geometric and dosimetric accuracy of DIR on DS1 and DS2 were performed, with a realistic biomechanical model derived DVF serving as ground truth. A patient specific DIR QA workflow was developed for a pancreatic patient cohort, and it has potential to be implemented as a clinical workflow.

A Method to Simplify Patient-Specific QA of Single Isocenter Multiple Met SRS Plans

<h3>Purpose/Objective(s)</h3> Pre-treatment quality assurance of single isocenter multi-met stereotactic radiosurgery (SRS) plans is standardly performed by acquiring a dose measurement using a detector array or ion chamber and film. The major efficiency issue with QA of these plans is how many, and which of the multiple metastases (mets) should be measured. The purpose of this study was to develop a workflow using an independent 3D dose calculation (DC) and trajectory log (TL) QA software to efficiently determine which of the brain mets should be further evaluated by measurement. <h3>Materials/Methods</h3> 30 single isocenter multiple metastasis patient SRS plans with a total of 82 mets were retrospectively studied. The clinical SRS plans were generated with an automated dynamic conformal arc SRS planning system (TPS). All plans were sent to the DC software for calculation. The gamma analysis (3%, 1 mm), percent difference between TPS and DC calculated mean and max dose to PTV, and the dose difference of the centroid point of each PTV was recorded. All plans were delivered to a high-resolution diode array designed for SRS and measurements were used to perform a gamma analysis (3%, 1 mm). In addition, the trajectory logs of the QA delivery were used to calculate 3D dose distributions and the same metrics acquired from the DC software were calculated and recorded. Pearson correlation between DC and TL calculated metrics was calculated. <h3>Results</h3> The results of the dosimetric metrics calculated by the DC and TL software are presented in Table 1. Overall agreement between DC and TPS was excellent, with all mean percent differences within 1.1% and mean gamma pass rates greater than 99%. Furthermore, dosimetric metrics calculated by DC and TL were highly correlated, with the correlation coefficients for all metrics at least 0.96, which suggests DC results can be used to predict TL QA results. To use DC to screen which PTVs should be further evaluated, an action level was set such that if any metric fell outside the 95% confidence interval (mean + 1.96 × SD), a QA measurement would be performed. For all metrics, a ±5% action level was calculated to cover the 95% confidence interval. DC results for 6 PTVs exceeded the action level. Of these plans, the measured patient specific QA passed for all plans. While all measured QA passed, DC and TL QA appeared to be more sensitive than the diode array measurement in identifying potential plan quality issues. <h3>Conclusion</h3> An efficient workflow for patient specific QA of single isocenter multiple met SRS plans was developed. An independent 3D dose calculation is used to calculate agreement of dosimetry metrics compared to the TPS. If the percent difference of a metric for a PTV exceeds a 5% action level, it is selected for further QA using a dose measurement.

Radiotherapy Physics Quality Assurance and Management Practices in Less Resourced Facilities: An Initial Pilot Survey in Six Countries Followed by an Onsite Visit and Audit of Radiotherapy Centers in a Lower Middle-Income Country (LMIC)

<h3>Purpose/Objective(s)</h3> To assess physics quality assurance (PQA) and practices in less resourced RT clinics to improve the quality of care. <h3>Materials/Methods</h3> An initial survey was conducted in 2020 to thirteen select RT centers in six countries. In 2021, from the survey outcome, our team conducted onsite visits to all the RT-centers in one of the countries: one private, and two public (Public-1- and –2). Public-1 in the capital and Public-2 in a regional capital. Follow-up surveys were sent to 17 medical physicists. Questions centered on the topic of equipment, institutional practice, physics quality assurance, management, and safety practices. Qualitative and descriptive methods were used for data analysis. We accessed operational challenges using a 5-point Likert system: machine downtime, patient related issues, power outage and staffing. <h3>Results</h3> We received a 100% response rate. Six (6/17) had 1-3 years of experience and two (2/17) have over 15 years of experience. Over 80% of respondents had experience with a treatment planning system. Private and public-1 have CT simulators located in RT clinic. Breast, genitourinary, Gynecologic, H&N were the most treated diseases in all three centers. All three (3) clinics have 3D conformal RT (3D-CRT), the private and public-2 have electron treatment modalities and only public-1 have IMRT treatment available. An audit during the visit showed that no single point diode patient specific QA is done for 3D-CRT patients and none of the institutions had a functioning diode to carry out this QA process. The average daily patient workload on external beam device for each clinic was LINAC: Private = 25, public-1 = 55, public-2 = 40; Co- 60: public-1 = 45, Public-2 = 25. Public-1 and-2 lacked the equipment necessary to conform to best practices in TG-142 and 198. Public-2 reported significant challenges in the operational challenges above with values of 4.5, 3 and 2.75 and 2.25. Notably Public-1 and-2 have peer review chart rounds which are attended by clinical oncologists, medical physicists, physicians, and physics trainees. All (17/17) responded to having a system of documenting, tracking, and trending patient related safety incidents but only one (1/17) physicist reported using a voluntary incident reporting system. Although not quantifiable audits during site visits show education and training remains the most important need in operating successful local PQA and management programs. In the initial survey RT centers in lower GNIpc country there was a direct correlation between QA needs and the country index. <h3>Conclusion</h3> The initial study showed a direct correlation between QA needs and the development index of a country which has led to the first of a continent-wide survey intended to spotlight PQA practices in LIMCs, the challenges faced, lessons learned, to help understand the gaps and needs to better support their local PQA and management programs.

Fabrication of 3D printed head phantom using plaster mixed with polylactic acid powder for patient-specific QA in intensity-modulated radiotherapy

This study aimed to fabricate a heterogeneous phantom replicating the commercial Rando phantom by mixing plaster powder and polylactic acid (PLA) powder. Producing a heterogeneous phantom using Plaster and PLA is cheaper because it can be easily obtained in the commercial market. Additionally, patient-specific Quality Assurance can be easily performed because the phantom can be produced based on the patient’s CT image. PLA has been well studied in the field of radiation therapy and was found to be safe and effective. To match the mean Hounsfield unit (HU) values of the Rando phantom, the bone tissue was changed using plaster and 0–35% PLA powder until an appropriate HU value was obtained, and soft tissue was changed using the PLA infill value until an appropriate HU value was obtained. Bone tissue (200 HU or higher), soft issue (− 500 to 200 HU), and air cavity (less than − 500 HU) were modeled based on the HU values on the computed tomography (CT) image. The bone tissue was modeled as a cavity, and after three-dimensional (3D) printing, a solution containing a mixture of plaster and PLA powder was poured. To evaluate the bone implementation of the phantom obtained by the mixture of plaster and PLA powder, the HU profile of the CT images of the 3D-printed phantom using only PLA and the Rando phantom printed using only PLA was evaluated. The mean HU value for soft tissue in the Rando phantom (− 22.5 HU) showed the greatest similarity to the result obtained with an infill value of 82% (− 20 HU). The mean HU value for bone tissue (669 HU) showed the greatest similarity to the value obtained with 15% PLA powder (680 HU). Thus, for the phantom composed of plaster mixed with PLA powder, soft tissue was fabricated using a 3D printer with an infill value of 82%, and bone tissue was fabricated with a mixture containing 15% PLA powder. In the HU profile, this phantom showed a mean difference of 61 HU for soft tissue and 109 HU for bone tissue in comparison with the Rando phantom. The ratio of PLA powder and plaster can be adjusted to achieve an HU value similar to bone tissue. A simple combination of PLA powder and plaster enabled the creation of a custom phantom that showed similarities to the Rando phantom in both soft tissue and bone tissue.

Prediction and classification of VMAT dosimetric accuracy using plan complexity and log-files analysis

PurposeWe presented different machine learning models based on log files analysis and complexity indexes to predict and classify the dosimetric accuracy of VMAT plans. MethodsA total of 1302 VMAT arcs from 651 treatment plans were analyzed using the modulation complexity score (MCS) and the dynamic log-files generated by the linac. Predicted and measured fluences were compared using γ-analysis in terms of mean γ-values (γmean) and γ-pass rate (γ%). A kernel regression model was developed aiming to predict individual γ% and γmean values. Multinomial logistic regression (LR), Naïve-Bayes (NB) and support vector machine (SVM) models were developed based on MCS values to classify QA results as “pass” (γ%greater than90 % and γmean < 0.5), “control” (80 % < γ% < 90 % and 0.50 < γmean < 0.75) and “fail” (γ% < 80 % and γmean > 0.75). Training, validation and testing groups were used to evaluate the model reliability. A complexity-based traffic light protocol was implemented to flag pass (green light), control (orange light) and failed plans (red light). ResultsPrediction accuracy of residuals for γ% was 2.1 % and 2.2 % in the training and testing cohorts, respectively. For 2 %(local)/2mm, both γ% and γmean classification performances reported weighted precision, recall and F1-values greater than 90 % for all machine learning models. The optimal MCS threshold value for the identification of failed plans was 0.130, with a sensibility and specificity of 0.994 and 0.952, respectively. The optimal MCS threshold for reliable plans was 0.270, with a sensitivity and specificity of 0.925 and 0.922, respectively. ConclusionsMachine learning can accurately predict the dosimetric accuracy of VMAT treatments, representing an efficient tool to assist patient-specific QA.

Preclinical validation and treatment of volumetric modulated arc therapy based total bone marrow irradiation in Halcyon™ ring gantry linear accelerator

AimThis study aims​ to report preclinical validation, and the first clinical treatment of total bone marrow irradiation (TMI) and total bone marrow and lymph nodal irradiation (TMLI) using Volumetric modulated arc therapy in Halcyon-E ring gantry linear accelerator. Preclinical validation includes simulation, planning, patient-specific QA, and dry run.Material and methodFour patients, two female and two male, with body weights of 116 kg, 52 kg, 64 kg, and 62 kg; with two with chronic myeloid leukemia, one each with acute lymphoblastic leukemia and acute myeloid leukemia (AML) were simulated and planned for TMI/TMLI. Patients were immobilized with a full-body vacuum bag. Head first supine (HFS) and Feet first supine (FFS) CT scans were acquired from head to knee and knee to toe. Planning target volume (PTV) was created with a uniform margin of 6 mm over the total bone marrow/bone marrow + lymph nodes. HFS and FFS PTVs were optimized independently using 6MV unflatten energy for 12 Gy in 6 fractions. Plans were merged to create the resultant dose distribution using a junction bias dose matching technique. The total number of isocenters was ≤ 10 per CT set, and two to four full arcs were used for each isocenter. A junction dose gradient technique was used for dose feathering between arcs between adjacent isocenters.ResultOnly one female patient diagnosed as AML received the TMLI treatment, while the other three patients dropped out due to clinical complications and comorbidities that developed in the time between simulation and treatment. The result presented has been averaged over all four patients. For PTV, 95% dose was normalised to 95% volume, PTV_V107% receiving 3.3 ± 3.1%. Total lung mean and V12Gy were 1048.6 ± 107.1 cGy and 19.5 ± 12.1%. Maximum lens doses were 489.5 ± 35.5 cGy (left: L) and 497 ± 69.2 cGy (right: R). The mean cardiac and bilateral kidney doses were 921.75 ± 89.2 cGy, 917.9 ± 63.2 cGy (L), and 805.9 ± 9.7 cGy (R). Average Monitor Unit was 7738.25 ± 1056.6. The median number of isocenters was 17(HFS+FFS), average MU/Dose (cGy) ratio per isocenter was 2.28 ± 0.3.ConclusionHalcyon-E ring gantry linear accelerator capable of planning and delivering TMI/TMLI.​​

IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance

The IBA myQA® SRS high-resolution solid-state detector was evaluated in the context of robotic radiosurgery delivered using CyberKnife®. The performance was investigated for periodic machine delivery quality assurance (DQA) and patient-specific treatment verification. myQA® SRS is a 140 × 120 mm CMOS matrix with 400 µm resolution, allocated in a cylindrical ABS phantom topped by a hemispheric cap. Evaluations included: periodic DQA tests, angular response, dose-rate dependence and Iris variable aperture collimator field size measurements. For patient-specific QA various intracranial targets were studied (Gamma Index, 3%/1 mm agreement criteria), taking into account also the detector’s angular response. Results for periodic DQA were in accordance with the machine commissioning data. Dose-rate dependence was confirmed, and angular response tests resulted in a signal decay &gt;5% when beams were delivered outside a ±50° amplitude cone with respect to the vertical direction. Concerning patient-specific QA, &gt;50° angled beams elimination led to a Gamma Index passing rates improvement ranging between +3% and +115%. IBA myQA® SRS proved to be a suitable device for many CyberKnife® constancy DQA checks, providing high-resolution real-time results. Patient-specific Gamma tests showed high passing rates once angular dependence corrections were performed, even in high complexity treatments such as those for trigeminal neuralgia targets.

Dosimetric Evaluation of the Treatment Plan on Indigenous Heterogeneous Phantoms using Analytical Anisotropic Algorithm and Acuros-XB Algorithm for Different Photon Energies.

Background: Modern radiotherapy techniques are using advanced algorithms; however, phantoms used for quality assurance have homogeneous density; accordingly, the development of heterogeneous phantom mimicking human body sites is imperative to examine variation between planned and delivered doses.Objective: This study aimed to analyze the accuracy of planned dose by different algorithms using indigenously developed heterogeneous thoracic phantom (HT).Material and Methods: In this experimental study, computed tomography (CT) of HT was done, and the density of different parts was measured. The plan was generated on CT images of HCP with 6 and 15 Megavoltage (MV) photon beams using different treatment techniques, including three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Plans were delivered by the linear accelerator, and the dose was measured using the ion chamber (IC) placed in HT; planned and measured doses were compared.Results: Density patterns for different parts of the fabricated phantom, including rib, spine, scapula, lung, chest wall, and heart were 1.849, 1.976, 1.983, 0.173, 0.855, and 0.833 g/cc, respectively. Variation between planned and IC estimated doses with the tolerance (±5%) for all photon energies using different techniques. Acuros-XB (AXB) showed a slightly higher variation between computed and IC estimated doses using HCP compared to the analytical anisotropic algorithm (AAA).Conclusion: The indigenous heterogeneous phantom can accurately simulate the dosimetric scenario for different algorithms (AXB or AAA) and be also utilized for routine patient-specific QA.

Evaluating suggested stricter gamma criteria for linac-based patient-specific delivery QA in the conventional and SBRT environments

PurposeTo evaluate AAPM TG-218 recommended tolerances for IMRT QA for conventional and SBRT delivery. MethodsQA analysis was repeated for 150 IMRT/VMAT patients with varying gamma criteria. True composite delivery was utilized, corrected for detector and output variation. Universal tolerance (TLuniv) and action limits (ALuniv) were compared with statistical process control (SPC) TLSPC and ALSPC values.Analysis was repeated as a function of plan complexity for 250 non-stereotactic body radiotherapy (SBRT) VMAT patients at 3%/2mm and a threshold of 10% and for 75 SBRT VMAT patients at 2%/2 mm and a threshold of 50% with results plotted as a function of PTV volume. Regions of failure were dose-scaled on the planning CT data sets based on delivery results. ResultsThe IMRT/VMAT TLSPC and ALSPC for gamma criteria of 3%/3 mm were 96.5% and 95.6% and for 3%/2 mm were 91.2% and 89.2%, respectively. Correlation with plan complexity for conventional fractionation VMAT was “low” for all sites with pelvis having the highest r value at −0.35. The equivalent SBRT PTV diameter ranged from 2.0 cm to 5.6 cm. Negative low correlation was found for 38 of 75 VMAT cases below ALuniv. ConclusionsThe ALuniv and ALSPC are similar for 3%/2 mm. However, our 5% failure rate for ALuniv, may result in treatment start delays approximately 2 times/month, given 40 new cases/month. VMAT QA failure at stricter criteria did not correlate strongly with plan complexity. Site-specific action limits vary less than 3% from the average. SBRT QA results do not strongly correlate with target size over the range studied.