Pre-treatment Quality Assurance Research Articles

Dosimetric Systems in Pre-Treatment QA for Stereotactic Treatments: Correlation Agreements and Target Volume Dependency.

This study comprehensively investigated pre-treatment quality assurance (QA) for 100 cancer patients undergoing stereotactic treatments (SRS/SRT) using various detectors. The study conducted QA for SRS/SRT treatments planned with a 6MV SRS beam at a dose rate of 1,000 MU/min, utilizing Eclipse v13.6 Treatment Planning System (TPS). Point dose measurements employed 0.01cm3 and 0.13cm3 cylindrical ionization chambers, while planar dose verification utilized Gafchromic EBT-XD Film and Portal Imager (aS1000). Plans were categorized by target volume, and a thorough analysis compared point dose agreements, planar dose gamma pass rates, and their correlations with chamber volume mean dose, detector type, and point dose agreement. Additionally, the consistency between different ionization chambers was assessed. Point dose agreement generally improved with increasing target volume, except for volumes over 10cm3 with 0.01cm3 chambers, showing a contrary trend. Significant differences (p<0.05) were observed between TPS and measured doses for both chambers. Gamma pass rate improved with increasing target volume in EBT XD and aS1000 analyses, except for the >10cm3 group in EBT XD. EBT XD demonstrated better agreement with TPS for target volumes up to 10cm3 compared to aS1000, with a statistically significant difference (p<0.05) between the detectors. Strong correlations were found between chamber point dose and chamber volume mean dose agreement, as well as between the two gamma criteria analyses of the same detector type in the planar dose correlation analysis. However, weak correlations were discovered for other analyses. This study found weak correlation between different detector types in pre-treatment QA for point dose and planar dose evaluation. However, within a specific detector type, strong correlation was observed for different point dose evaluation methods and gamma criteria. This highlights the importance of cautious interpretation of QA results, particularly for SRS QA, due to the lack of correlation between detector types.

Characterization of a flexible a-Si:H detector for in vivo dosimetry in therapeutic x-ray beams.

The increasing use of complex and high dose-rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre-treatment quality assurance (QA) measurements alone, many countries are now mandating the use of in vivo dosimetry, whereby a dosimeter is placed on the surface of the patient during treatment. Ideally, in vivo detectors should be flexible to conform to a patient's irregular surfaces. This study aims to characterize a novel hydrogenated amorphous silicon (a-Si:H) radiation detector for the dosimetry of therapeutic x-ray beams. The detectors are flexible as they are fabricated directly on a flexible polyimide (Kapton) substrate. The potential of this technology for application as a real-time flexible detector is investigated through a combined dosimetric and flexibility study. Measurements of fundamental dosimetric quantities were obtained including output factor (OF), dose rate dependence (DPP), energy dependence, percentage depth dose (PDD), and angular dependence. The response of the a-Si:H detectors investigated in this study are benchmarked directly against commercially available ionization chambers and solid-state diodes currently employed for QA practices. The a-Si:H detectors exhibit remarkable dose linearities in the direct detection of kV and MV therapeutic x-rays, with calibrated sensitivities ranging from (0.580±0.002) pC/cGy to (19.36±0.10) pC/cGy as a function of detector thickness, area, and applied bias. Regarding dosimetry, the a-Si:H detectors accurately obtained OF measurements that parallel commercially available detector solutions. The PDD response closely matched the expected profile as predicted via Geant4 simulations, a PTW Farmer ionization chamber and a PTW ROOS chamber. The most significant variation in the PDD performance was 5.67%, observed at a depth of 3mm for detectors operated unbiased. With an external bias, the discrepancy in PDD response from reference data was confined to±2.92% for all depths (surface to 250mm) in water-equivalent plastic. Very little angular dependence is displayed between irradiations at angles of 0° and 180°, with the most significant variation being a 7.71% decrease in collected charge at a 110° relative angle of incidence. Energy dependence and dose per pulse dependence are also reported, with results in agreement with the literature. Most notably, the flexibility of a-Si:H detectors was quantified for sample bending up to a radius of curvature of 7.98mm, where the recorded photosensitivity degraded by (-4.9±0.6)% of the initial device response when flat. It is essential to mention that this small bending radius is unlikely during in vivo patient dosimetry. In a more realistic scenario, with a bending radius of 15-20mm, the variation in detector response remained within±4%. After substantial bending, the detector's photosensitivity when returned to a flat condition was (99.1±0.5)% of the original response. This work successfully characterizes a flexible detector based on thin-film a-Si:H deposited on a Kapton substrate for applications in therapeutic x-ray dosimetry. The detectors exhibit dosimetric performances that parallel commercially available dosimeters, while also demonstrating excellent flexibility results.

Feasibility of Ethos adaptive treatments of lung tumors and associated quality assurance.

Online adaptive radiotherapy with Ethos is based on the anatomy determined from daily cone beam computed tomography (CBCT) images. Dose optimization and computation are performed on the density map of a synthetic CT (sCT), a deformable registration of the initial planning CT (pCT) onto the current CBCT. Large density changes as present in the lung region are challenging the system. Treatment plans for Ethos were created and delivered for 1, 2, and 3cm diameter lung lesions in an anthropomorphic phantom, combining different insets in the pCT and during adaptive and non-adaptive treatment sessions. Primary and secondary dose calculations as well as back-projected dose from portal images were evaluated. Density changes due to changed insets were not considered in the sCTs. This resulted in errors in the dose; for example, -15.9% of the mean dose for a plan when changing from a 3cm inset in the pCT to 1cm at the time of treatment. Secondary dose calculation is based on the sCT and could therefore not reveal these dose errors. However, dose calculation on the CBCT, either as a recalculation in the treatment planning system or as pre-treatment quality assurance (QA) before the treatment, indicated the differences. EPID in-vivo QA also reported discrepancies between calculated and delivered dose distributions. An incorrect density distribution in the sCT has an impact on the dose calculation accuracy in the adaptive treatment workflow with the Ethos system. Additional quality checks of the sCT can detect such errors.

ACPSEM position paper: pre-treatment patient specific plan checks and quality assurance in radiation oncology.

The Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) has not previously made recommendations outlining the requirements for physics plan checks in Australia and New Zealand. A recent workforce modelling exercise, undertaken by the ACPSEM, revealed that the workload of a clinical radiation oncology medical physicist can comprise of up to 50% patient specific quality assurance activities. Therefore, in 2022 the ACPSEM Radiation Oncology Specialty Group (ROSG) set up a working group to address this issue. This position paper authored by ROSG endorses the recommendations of the American Association of Physicists in Medicine (AAPM) Task Group 218, 219 and 275 reports with some contextualisation for the Australia and New Zealand settings. A few recommendations from other sources are also endorsed to complete the position.

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatment Quality Assurance (QA) measurement. Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with>5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1mm / 10% threshold gamma pass rate (GPR)=99.4±1.9%vs. 93.1±13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1mm / 10% threshold) exceeding 90% (71.5%±16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5%±3.3%, p=0.01). Independent MC complements pre-treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre-treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre-treatment QA measurement.

Intraoperative radiation therapy with 50kV x-rays: A multi-institutional review.

This report covers clinical implementation of a low kV intraoperative radiation therapy (IORT) program with the INTRABEAM® System (Carl Zeiss Meditec AG, Jena, Germany). Based on collective user experience from eight institutions, we discuss best methods of INTRABEAM quality assurance (QA) tests, commissioning measurements, clinical workflow, treatment planning, and potential avenues for research. The guide provides pertinent background information and clinical justification for IORT. It describes the INTRABEAM system and commissioning measurements along with a TG100 risk management analysis to ensure safety and accuracy of the IORT program. Following safety checks, dosimetry measurements are performed for verification of field flatness and symmetry, x-ray output, and depth dose. Also discussed are dose linearity checks, beam isotropy, ion chamber measurements, calibration protocols, and in-vivo dosimetry with optically stimulated luminescence dosimeters OSLDs, and radiochromic film. Emphasis is placed on the importance of routine QA procedures (daily, monthly, and annual) performed at regular intervals for a successful IORT program. For safe and accurate dose delivery, tests of important components of IORT clinical workflow are emphasized, such as, dose prescription, pre-treatment QA, treatment setup, safety checks, radiation surveys, and independent checks of delivered dose. Challenges associated with in-vivo dose measurements are discussed, along with special treatment procedures and shielding requirements. The importance of treatment planning in IORT is reviewed with reference to a Monte Carlo-based commercial treatment planning system highlighting its main features and limitations. The report concludes with suggested topics for research including CT-based image-guided treatment planning and improved prescription dose accuracy. We hope that this multi-institutional report will serve as a guidance document on the clinical implementation and use of INTRABEAM IORT.

Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy

Background and purposeTreatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA). Material and methodsA number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA. ResultsIntra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Inter-institutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute. ConclusionsThe study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type.

Patient specific quality assurance of volumetric modulated arc therapy of synchronous bilateral breast cancer

Synchronous bilateral breast cancers (SBBC) present a considerable issue in external beam radiotherapy because of large fields size and large target volumes. Mono-isocentric volumetric modulated arc therapy (VMAT) appears as an appropriate irradiation technique for these types of tumors. The aim of this study was to demonstrate the utility of a 3D DVH pretreatment quality assurance program in VMAT of SBBC cases. Twenty SBBC patients who underwent radiation therapy in our department were retrospectively enrolled in this study. Fifteen patients were treated exclusively to the mammary glands. Five patients benefited from a dose boost on the tumor bed (60Gy). Nine patients were irradiated on the supraclavicular nodes (50Gy). This dose was delivered in 25 fractions and integrated boost was used when appropriate. Depending on the complexity of the treatment plans; 2 or 4 arcs VMAT plans were used in a mono-isocentric technique. The patient specific quality assurance (PSQA) was evaluated using COMPASS measured data, COMPASS reconstructed (CR) and COMPASS computed (CC) dose compared to treatment planning system (TPS) dose. Clinical evaluation was based on DVH metrics for target volumes and organ at risks.The maximum average dose deviation between TPS, CC, and CR was below 3%. The paired t-test between TPS, CC, and CR shows a strong agreement (p < 0.001). The 3DVH dose distribution comparison between TPS and COMPASS were also performed with good gamma score for global analysis.COMPASS was successfully evaluated as a 3DVH pretreatment system for SBBC despite the large fields size and complex target volumes. It allows the verification of the plan in 3D patient anatomy and the evaluation of dose discrepancies.

Feasibility of MPC enhanced couch as SRS/SBRT Pretreatment QA

Purpose: The purpose of this study was to investigate the feasibility of using the Winston-Lutz (WL) interchangeability with a Machine Performance Check-enhanced couch for pretreatment quality assurance (QA) of stereotactic radiotherapy (SRS) and stereotactic body radiation therapy (SBRT). The study employed the MPC with an electronic portal imaging device (EPID) to carry out geometric checks and verify the radiation isocenter. The isocenter size was assessed using the MultiMet cube and the MPC-enhanced couch module for SRS/SBRT pretreatment QA. Methods: The isocenter size of the MPC-enhanced couch module was compared to the WL measurements of the MultiMet cube. Measurements were taken at various gantry, collimator, and couch angles over a period of one month. The data from the cube were evaluated using PIPSRO and MultiMet (MMWL), including the offset targets. Various statistical tests were performed to evaluate the agreement, normality, separability, sensitivity, and specificity between the two methods. Results: The results showed isocenter sizes of 0.273 ± 0.065 mm, 0.293 ± 0.010 mm, and 0.209 ± 0.070 mm for PIPSPRO, MPC, and MMWL, respectively. The average bias was -0.0639 ± 0.1061 mm between MMWL and PIPSPRO, -0.0837 ± 0.0688 mm between MMWL and MPC, and 0.0198 ± 0.0696 mm between MPC and PIPSPRO. A Shapiro-Wilk test revealed no significant departure from normality for all tests and showed satisfactory discrimination through the area under the curve (AUC). A paired t-test analysis revealed a statistically significant difference between the mean isocenter size of the MPC and WL (MMPWL: t = 5.654, df = 29, p &lt; 0.001; PIPSPRO: t = -1.483, DF = 29, p = 0.1488), and there was no significant difference within the WL test (t = 3.008, DF = 29, p = 0.0054). Conclusion: Despite the statistical test results, there was agreement between the MPC and WL radiation isocenter size that was within the requirement of the AAPM TG 142 tolerance (±1.0 mm). The MPC proved to be accurate, reproducible, and consistent throughout the measurements, making it an appropriate and effective pretreatment QA tool for SRS/SBRT.

Is it still necessary to perform measured based pre-treatment patient-specific QA for SRS HyperArc treatments?

The Mobius3D system was validated as a modern secondary check dosimetry system. In particular, our objective has been to assess the suitability of the M3D as pre-treatment patient-specific Quality Assurance (QA) tool for Stereotactic Radiosurgery (SRS) HyperArc (HA) treatments. We aimed to determine whether Mobius3D could safely replace the measurements-based patient-specific QA for this type of treatment. 30 SRS HA treatment plans for brain were selected. The dose distributions, calculated by Mobius and our routinely used algorithm (AcurosXB v.15.6), were compared using gamma analysis index and DVH parameters based on the patient's CT dataset. All 30 plans were then delivered across the ionization chamber in a homogeneous phantom and the measured dose was compared with both M3D and TPS calculated one. The plans were delivered and verified in terms of PSQA using the electronic portal imaging device (EPID) with Portal Dosimetry (PD) and myQA SRS (IBA Dosimetry) detector. Plans that achieved a global gamma passing rate (GPR) ≥ 97% based on 2%/2mm criteria, with both Mobius3D and the conventional methods were evaluated acceptable. Finally, we assessed the capability of the M3D system to detect errors related to the position of the Multi-Leaf Collimator (MLC) in comparison to the analyzed measurement-based systems. No relevant differences were observed in the comparison between the dose calculated on the CT-dataset by M3D and the TPS. Observed discrepancies are imputable to different used algorithms, but no discrepancies related to goodness of plans have been found. Average differences between calculated (M3D and TPS) vs measured dose with ionization chamber were 2.5% (from 0.41% to 3.2%) and 1.81% (from 0.66% to 2.65%), for M3D and TPS, respectively. All plans passed with a gamma passing rate>97% using conventional PSQA methods with a gamma criterion of 2% dose difference and 2mm distance-to-agreement. The average gamma passing rate for the M3D system was determined to be 99.4% (from 97.3% to 100%). Results from this study also demonstrated Mobius has better error detectability than conventional measurement-based systems. Our study shows Mobius3D could be a suitable alternative to conventional measured based QA methods for SRS HyperArc treatments.

Sensitivity and specificity of Monte Carlo based independent secondary dose computation for detecting modulation-related dose errors in intensity modulated radiotherapy

BackgroundThe recent availability of Monte Carlo based independent secondary dose calculation (ISDC) for patient-specific quality assurance (QA) of modulated radiotherapy requires the definition of appropriate, more sensitive action levels, since contemporary recommendations were defined for less accurate ISDC dose algorithms. PurposeThe objective is to establish an optimum action level and measure the efficacy of a Monte Carlo ISDC software for pre-treatment QA of intensity modulated radiotherapy treatments. MethodsThe treatment planning system and the ISDC were commissioned by their vendors from independent base data sets, replicating a typical real-world scenario. In order to apply Receiver-Operator-Characteristics (ROC), a set of treatment plans for various case classes was created that consisted of 190 clinical treatment plans and 190 manipulated treatment plans with dose errors in the range of 1.5–2.5%. All 380 treatment plans were evaluated with ISDC in the patient geometry. ROC analysis was performed for a number of Gamma (dose-difference/distance-to-agreement) criteria. QA methods were ranked according to Area under the ROC curve (AUC) and optimum action levels were derived via Youden’s J statistics. ResultsOverall, for original treatment plans, the mean Gamma pass rate (GPR) for Gamma(1%, 1 mm) was close to 90%, although with some variation across case classes. The best QA criterion was Gamma(2%, 1 mm) with GPR > 90% and an AUC of 0.928. Gamma criteria with small distance-to-agreement had consistently higher AUC. GPR of original treatment plans depended on their modulation degree. An action level in terms of Gamma(1%, 1 mm) GPR that decreases with modulation degree was the most efficient criterion with sensitivity = 0.91 and specificity = 0.95, compared with Gamma(3%, 3 mm) GPR > 99%, sensitivity = 0.73 and specificity = 0.91 as a commonly used action level. ConclusionsISDC with Monte Carlo proves highly efficient to catch errors in the treatment planning process. For a Monte Carlo based TPS, dose-difference criteria of 2% or less, and distance-to-agreement criteria of 1 mm, achieve the largest AUC in ROC analysis

Pilot scale validation campaign of gel dosimetry for pre-treatment quality assurance in stereotactic radiotherapy

PurposeComplex stereotactic radiotherapy treatment plans require prior verification. A gel dosimetry system was developed and tested to serve as a high-resolution 3D dosimeter for Quality Assurance (QA) purposes. Materials and MethodsA modified version of a polyacrylamide polymer gel dosimeter based on chemical response inhibition was employed. Different sample geometries (cuvettes and phantoms) were manufactured for calibration and QA acquisitions. Irradiations were performed with a Varian Trilogy linac, and analyses of irradiated gel dosimeters were performed via MRI with a 1.5 T Philips Achieva at 1 mm3 or 2 mm3 isotropic spatial resolution. To assess reliability of polymer gel data, 54 stereotactic clinical treatment plans were delivered both on dosimetric gel phantoms and on the Delta4 dosimeter. Results from the two devices were evaluated through a global gamma index over a range of acceptance criteria and compared with each other. ResultsA quantitative and tunable control of dosimetric gel response sensitivity was achieved through chemical inhibition. An optimized MRI analysis protocol allowed to acquire high resolution phantom dose data in timeframes of ≈ 1 h. Conversion of gel dosimeter data into absorbed dose was achieved through internal calibration. Polymer gel dosimeters (2 mm3 resolution) and Delta4 presented an agreement within 4.8 % and 2.7 % at the 3 %/1 mm and 2 %/2 mm gamma criteria, respectively. ConclusionsGel dosimeters appear as promising tools for high resolution 3D QA. Added complexity of the gel dosimetry protocol may be justifiable in case of small target volumes and steep dose gradients.

Complexity Metrics and Planning Dose-Based Pretreatment Patient-Specific Quality Assurance Prediction: Classification, Gamma Passing Rates, and DVH Deviation

Patient-specific quality assurance (QA) prediction before treatment is beneficial to the clinical resource allocation and the dosimetric monitoring of the patient plans. The aim of this study is to investigate the potential of complexity metrics of radiotherapy plan and patient planning dose to predict QA result, gamma passing rates and dose-volume indices deviation. Planning dose from treatment planning system (TPS), reconstructed dose from a vendor provided QA phantom and complexity metrics of the 499 radiotherapy plans of patients in our institution from March 2022 to September 2022 were used for methodology verification. Gamma passing rate (3%/2mm,10% threshold) 90% was regarded as criterion of QA pass or fail. A deep learning model ResNet-50 was modified to 3D dose processing and a multilayer perceptron (MLP) with three layers were adopted to extract features from 3D dose and 1D metrics in two parallel ways, then, the features were concatenate together to predict QA results. The dataset was split into 349 for train, 50 for validation and 100 for testing. Evaluation of predictions was based on absolute value deviation and area under the curves (AUC) of receiver operator characteristic (ROC) curve. In this dataset, 71% (355/499) plans pass the pretreatment QA test. For QA passing prediction in 100 testing cases, the AUC of ROC could achieve 0.92. For gamma passing rates prediction, a mean absolute error (MAE) of 1.8% could be observed for cases with gamma passing rates bigger than 90%, and a MAE of 4.5% deviation could be observed for cases with gamma passing rates from 80% to 90%. For PTV ΔD95 (%) and PTV ΔHI (%), the MAE of prediction and ground truth is 1%. The model with only complexity metrics and only 3D dose could achieve the AUC of ROC 0.91 and 0.84, respectively. The complexity metrics and 3D planning dose-based model could predict pretreatment patient specific QA results with high accuracy and the complexity metrics play a leading role in the model. Dose-volume metrics deviations of PTV could be predicted and more clinically useful information could be provided.

TROG 15.03/ANZUP International Multicenter Phase II Trial of Focal Ablative STereotactic RAdiotherapy for Cancers of the Kidney (FASTRACK II)

Stereotactic body radiotherapy (SBRT) is an emerging non-invasive alternative for primary renal cell cancer (RCC) in patients unsuitable for surgery. The objective of the FASTRACK II clinical trial was to investigate the efficacy of SBRT for primary RCC. This non-randomized, intergroup multi-institutional phase II study was activated in 7 Australian centers and 1 Dutch center, through the Trans Tasman Radiation Oncology Group (TROG) and the Australian and New Zealand Urogenital and Prostate Cancer Trials Group (ANZUP). Eligible patients had biopsy confirmed diagnosis of primary RCC with a single lesion within a kidney, ECOG performance ≤2 and were medically inoperable, high risk or declined surgery. For tumors ≤4 cm a single fraction of 26 Gy was prescribed, for tumors > 4 cm, 42 Gy in three fractions was prescribed. The primary outcome of the study was to estimate the efficacy of SBRT for primary RCC, defined as local control based on RECIST criteria. The study was powered assuming that 1-year local control would be 90%, with the null hypothesis of ≤80% considered undesirable and not worthy of proceeding to a future randomized controlled trial. Between July 2016 and February 2020, 70 patients were enrolled with a median follow-up of 42 months. Median age was 77 years. Forty-nine patients were male (70%), median BMI was 32 and median Charlson comorbidity score was 7. The median [IQR] RENAL complexity score was 8 [7-10]. Biopsy confirmation was 100%. Twenty-three patients (33%) had T1a disease. The median (interquartile range [IQR]) tumor size was 4.6cm [3.7-5.5]; it was 3.3cm [3.0-3.6] in those receiving single fraction (n = 23), and 5.3cm [4.6-6.0] in those receiving 3-fraction SBRT (n = 47). During real-time pre-treatment quality assurance review, 10 cases (14.3%) required resubmission for protocol deviation, 2119 variables were assessed at final review, and final protocol compliance was 99.3%. Seven (10%) patients experienced grade 3 treatment-related adverse events, with no grade 4 or 5 events observed. Eleven (16%) patients reported no adverse events. Local control was 100% throughout the lifetime of the trial (p<0.001). Cancer-specific survival was also 100% throughout the lifetime of the trial. Freedom from distant failure (95% CIs) at 1 and 3 years was 99% (90-100%). Overall survival (95% CIs) at 1 and 3 years was 99% (90-100%) and 82% (70-89%), respectively. Baseline mean eGFR (95% CI) was 61.1 mLs/min (56.6; 65.6) and reduced by -10.8 mLs/min (-13.0; -8.6) by 1-year, by -14.6 mLs/min (-17.0; -12.2) by 2-years and plateaued thereafter. In the first multicenter prospective trial of a non-surgical primary RCC cohort, enrolling mostly T1b+ disease, SBRT was an effective treatment strategy with no observed local failures. We observed an acceptable side effect profile and renal function after SBRT. These outcomes support the design of a future randomized clinical trial of SBRT versus surgery for primary RCC. The trial was registered with ID: NCT02613819.

Monte Carlo simulation-based patient-specific QA using machine log files for line-scanning proton radiation therapy.

Quality assurance (QA) is a prerequisite for safe and accurate pencil-beam proton therapy. Conventional measurement-based patient-specific QA (pQA) can only verify limited aspects of patient treatment and is labor-intensive. Thus, a better method is needed to ensure the integrity of the treatment plan. Line scanning, which involves continuous and rapid delivery of pencil beams, is a state-of-the-art proton therapy technique. Machine performance in delivering scanning protons is dependent on the complexity of the beam modulations. Moreover, it contributes to patient treatment accuracy. A Monte Carlo (MC) simulation-based QA method that reflects the uncertainty related to the machine during scanning beam delivery was developed and verified for clinical applications to pQA. Herein, a tool for particle simulation (TOPAS) for nozzle modeling was used, and the code was commissioned against the measurements. To acquire the beam delivery uncertainty for each plan, patient plans were delivered. Furthermore, log files recorded every 60 μs by the monitors downstream of the nozzle were exported from the treatment control system. The spot positions and monitor unit (MU) counts in the log files were converted to dipole magnet strengths and number of particles, respectively, and entered into the TOPAS. For the 68 clinical cases, MC simulations were performed in a solid water phantom, and two-dimensional (2D) absolute dose distributions at 20-mm depth were measured using an ionization chamber array (Octavius 1500, PTW, Freiburg, Germany). Consequently, the MC-simulated 2D dose distributions were compared with the measured data, and the dose distributions in the pre-treatment QA plan created with RayStation (RaySearch Laboratories, Stockholm, Sweden). Absolute dose comparisons were made using gamma analysis with 3%/3mm and 2%/2mm criteria for 47 clinical cases without considering daily machine output variation in the MC simulation and 21 cases with daily output variation, respectively. All cases were analyzed with 90% or 95% of passing rate thresholds. For 47 clinical cases not considering daily output variations, the absolute gamma passing rates compared with the pre-treatment QA plan were 99.71% and 96.97%, and the standard deviations (SD) were 0.70% and 3.78% with the 3%/3mm or 2%/2mm criteria, respectively. Compared with the measurements, the passing rate of 2%/2mm gamma criterion was 96.76% with 3.99% of SD. For the 21 clinical cases compared with pre-treatment QA plan data and measurements considering daily output variations, the 2%/2mm absolute gamma analysis result was 98.52% with 1.43% of SD and 97.67% with 2.72% of SD, respectively. With a 95% passing rate threshold of 2%/2mm criterion, the false-positive and false-negative were 21.8% and 8.3% for without and with considering output variation, respectively. With a 90% threshold, the false-positive and false-negative reduced to 11.4% and 0% for without and with considering output variation, respectively. A log-file-based MC simulation method for patient QA of line-scanning proton therapy was successfully developed. The proposed method exhibited clinically acceptable accuracy, thereby exhibiting a potential to replace the measurement-based dosimetry QA method with a 90% gamma passing rate threshold when applying the 2%/2mm criterion.

Analysis of Radiation Therapy Quality Assurance in NRG Oncology RTOG 0848

NRG/Radiation Therapy Oncology Group 0848 is a 2-step randomized trial to evaluate the benefit of the addition of concurrent fluoropyrimidine and radiation therapy (RT) after adjuvant chemotherapy (second step) for patients with resected pancreatic head adenocarcinoma. Real-time quality assurance (QA) was performed on each patient who underwent RT. This analysis aims to evaluate adherence to protocol-specified contouring and treatment planning and to report the types and frequencies of deviations requiring revisions. In addition to a web-based contouring atlas, the protocol outlined step-by-step instructions for generating the clinical treatment volume through the creation of specific regions of interest. The planning target volume was a uniform 0.5 cm clinical treatment volume expansion. One of 2 radiation oncology study chairs independently reviewed each plan. Plans with unacceptable deviations were returned for revision and resubmitted until approved. Treatment started after final approval of the RT plan. From 2014 to 2018, 354 patients were enrolled in the second randomization. Of these, 160 patients received RT and were included in the QA analysis. Resubmissions were more common for patients planned with 3-dimensional conformal RT (43%) than with intensity modulated RT (31%). In total, at least 1 resubmission of the treatment plan was required for 33% of patients. Among patients requiring resubmission, most only needed 1 resubmission (87%). The most common reasons for resubmission were unacceptable deviations with respect to the preoperative gross target volume (60.7%) and the pancreaticojejunostomy (47.5%). One-third of patients required resubmission to meet protocol compliance criteria, demonstrating the continued need for expending resources on real-time, pretreatment QA in trials evaluating the use of RT, particularly for pancreas cancer. Rigorous QA is critically important for clinical trials involving RT to ensure that the true effect of RT is assessed. Moreover, RT QA serves as an educational process through providing feedback from specialists to practicing radiation oncologists on best practices.

A structured FMEA approach to optimizing combinations of plan-specific quality assurance techniques for IMRT and VMAT QA.

Many commercial tools are available for plan-specific quality assurance (QA) of radiotherapy plans, with their functionality assessed in isolation. However, multiple QA tools are required to review the full range of potential errors. It is important to assess their effectiveness in combination with each other to look for ways to both streamline the QA process and to make certain that errors of high impact and/or high occurrence are caught before reaching patient treatment. To develop a structured method to assess the effective risk reduction of combinations of QA methods for IMRT/VMAT treatments. First, a structured prospective risk assessment was performed to establish the major failure modes (FMs) of IMRT/VMAT QA, and assign occurrence (O), severity (S), and baseline detectability (BD) rankings to them. The baseline assumed that chart checks and linear accelerator QA was performed, but no plan-specific secondary dose calculation or measurement was done. Second, the detectability of each FM for two secondary dose calculation methods and four plan measurement methods (point-based dose calculation, Monte-Carlo-based dose calculation, 2D fluence-based measurement, 2.5D phantom-based measurement, log file analysis with dose recalculation, and log file analysis combined with MLC QA) was determined. Third, we used a minimum detectability approach in addition to each FM's occurrence and severity to determine the optimal combination of QA methods.We analyzed the cumulative risk priority number of eight combinations of QA methods. The analysis was done on (1) all FMs, (2) FMs with high severity, (3) FMs with high-risk priority numbers (RPN) of O*S*BD, and (4) on FMs with both high severity and high RPN. Our analysis resulted in 54 FMs, including commissioning, planning, data transfer, and linear accelerator failures. 1D secondary dose calculation plus measurement provided a 19%-22% risk reduction from baseline. 1D/3D secondary dose calculation plus log files created a 25%-32% reduction. 3D secondary dose calculation plus measurement resulted in a 27%-34% reduction. 3D secondary dose calculation plus log files with additional machine QA provided the greatest reduction of 31%-42%. This novel structured approach to comparing combinations of QA methods will allow us to optimize our procedures, with the goal of detecting all clinically significant FMs. Our results show that log-file QA with 3D dose recalculation and supplemental machine QA provides better risk reduction than measurement-based QA. This work builds evidence to justify reducing or eliminating measurement-based PSQA with an independent 3D dose verification, log-file measurement, and appropriate supplementation of machine QA. The process also highlights FMs that cannot be caught by pre-treatment QA, prompting us to consider future directions for on-treatment QA.

Localization of reference points in electromagnetic tracking data and their application for treatment error detection in interstitial breast brachytherapy.

Electromagnetic tracking (EMT) is a promising technology that holds great potential to advance patient-specific pre-treatment verification in interstitial brachytherapy (iBT). It allows easy determination of the implant geometry without line-of-sight restrictions and without dose exposure to the patient. What it cannot provide, however, is a link to anatomical landmarks, such as the exit points of catheters or needles on the skin surface. These landmarks are required for the registration of EMT data with other imaging modalities and for the detection of treatment errors such as incorrect indexer lengths, and catheter or needle shifts. To develop an easily applicable method to detect reference points in the positional data of the trajectory of an EMT sensor, specifically the exit points of catheters in breast iBT, and to apply the approach to pre-treatment error detection. Small metal objects were attached to catheter fixation buttons that rest against the breast surface to intentionally induce a local, spatially limited perturbation of the magnetic field on which the working principle of EMT relies. This perturbation can be sensed by the EMT sensor as it passes by, allowing it to localize the metal object and thus the catheter exit point. For the proof-of-concept, different small metal objects (magnets, washers, and bushes) and EMT sensor drive speeds were used to find the optimal parameters. The approach was then applied to treatment error detection and validated in-vitro on a phantom. Lastly, the in-vivo feasibility of the approach was tested on a patient cohort of four patients to assess the impact on the clinical workflow. All investigated metal objects were able to measurably perturb the magnetic field, which resulted in missing sensor readings, that is two data gaps, one for the sensor moving towards the tip end and one when retracting from there. The size of the resulting data gaps varied depending on the choice of gap points used for calculation of the gap size; it was found that the start points of the gaps in both directions showed the smallest variability. The median size of data gaps was ⩽8mm for all tested materials and sensor drive speeds. The variability of the determined object position was ⩽0.5mm at a speed of 1.0cm/s and ⩽0.7mm at 2.5cm/s, with an increase up to 2.3mm at 5.0cm/s. The in-vitro validation of the error detection yielded a 100% detection rate for catheter shifts of ≥2.2mm. All simulated wrong indexer lengths were correctly identified. The in-vivo feasibility assessment showed that the metal objects did not interfere with the routine clinical workflow. The developed approach was able to successfully detect reference points in EMT data, which can be used for registration to other imaging modalities, but also for treatment error detection. It can thus advance the automation of patient-specific, pre-treatment quality assurance in iBT.

Process Modeling a Radiation Oncology Clinic Workflow From Therapeutic Simulation to Treatment: Identifying Impending Strain and Possible Treatment Delays

The administration of safe, high-quality radiation therapy requires the systematic completion of a series of steps from computed tomography simulation, physician contouring, dosimetric treatment planning, pretreatment quality assurance, plan verification, and, ultimately, treatment delivery. Nevertheless, due consideration to the cumulative time required to complete each of these steps is often not given sufficient attention when determining patient start date. We set out to understand the systemic dynamics as to how varying patient arrival rate can affect treatment turnaround times using Monte Carlo simulations. We developed a process model workflow for a single physician, single linear accelerator clinic that simulated arrival rates and processing times for patients undergoing radiation treatment using the AnyLogic Simulation Modeling software (AnyLogic 8 University edition, v8.7.9). We varied the new patient arrival rate from 1 to 10 patients per week to understand the effect of treatment turnaround times from simulation to treatment. We used processing-time estimates determined in prior focus studies for each of the required steps. Altering the number of patients simulated from 1 per week to 10 per week resulted in a corresponding increase in average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patients from simulation to treatment ranged from 6 to 12 days. To compare individual distributions, we used the Kolmogorov-Smirnov statistical test.We found that altering the arrival rate from 4 patients per week to 5 patients per week resulted in a statistically significant change in the distributions of processing times (P=.03). The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery while ensuring quality and safety.

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream.