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.

Dosimetric impact of MLC positional errors on dose distribution in IMRT

Optimizing the positional accuracy of multileaf collimators (MLC) for radiotherapy is important for dose accuracy and for reducing doses delivered to normal tissues. This study investigates dose sensitivity variations and complexity metrics of MLC positional error in volumetric modulated arc therapy and determines the acceptable ranges of MLC positional accuracy in several clinical situations. Treatment plans were generated for four treatment sites (prostate cancer, lung cancer, spinal, and brain metastases) using different treatment planning systems (TPSs) and fraction sizes. Each treatment plan introduced 0.25-2.0 mm systematic or random MLC leaf bank errors. The generalized equivalent uniform dose (gEUD) sensitivity and complexity metrics (MU/Gy and plan irregularity) were calculated, and the correlation coefficients were assessed. Furthermore, the required tolerances for MLC positional accuracy control were calculated. The gEUD sensitivity showed the highest dependence of systematic positional error on the treatment site, followed by TPS and fraction size. The gEUD sensitivities were 6.7, 4.5, 2.5, and 1.7%/mm for Monaco and 8.9, 6.2, 3.4, and 2.3%/mm (spinal metastasis, lung cancer, prostate cancer, and brain metastasis, respectively) for RayStation. The gEUD sensitivity was strongly correlated with the complexity metrics (r = 0.88-0.93). The minimum allowable positional error for MLC was 0.63, 0.34, 1.02, and 0.28 mm (prostate, lung, brain, and spinal metastasis, respectively). The acceptable range of MLC positional accuracy depends on the treatment site, and an appropriate tolerance should be set for each treatment site with reference to the complexity metric. It is expected to enable easier and more detailed MLC positional accuracy control than before by reducing dose errors to patients at the treatment planning stage and by controlling MLC quality based on complexity metrics, such as MU/Gy.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to delivery errors

BACKGROUND

The use of modulated techniques for intra-cranial stereotactic radiosurgery (SRS) results in highly modulated fields with small apertures, which may be susceptible to uncertainties in the delivery device.

PURPOSE

This study aimed to quantify the impact of simulated delivery errors on treatment plan dosimetry and how this is affected by treatment planning system (TPS), plan geometry, delivery technique, and plan complexity. A beam modelling error was also included as context to the dose uncertainties due to treatment delivery errors.

METHODS

Delivery errors were assessed for multiple-target brain SRS plans obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets, each with a prescription of 20 Gy. Of the final dataset of 54 plans, 51 were created using the volumetric modulated arc therapy (VMAT) technique and three used intensity modulated arc therapy (IMRT). Thirty-five plans were from the Varian Eclipse TPS, 17 from Elekta Monaco TPS, and one plan each from RayStation and Philips Pinnacle TPS. The errors introduced included: monitor unit calibration errors, multi-leaf collimator (MLC) bank offset, single MLC leaf offset, couch rotations, and collimator rotations. Dosimetric leaf gap (DLG) error was also included as a beam modelling error. Dose to targets was assessed via dose covering 98% of planning target volume (PTV) (D98%), dose covering 2% of PTV (D2%), and dose covering 99% of gross tumor volume (GTV) (D99%). Dose to organs at risk (OARs) was assessed using the volume of normal brain receiving 12 Gy (V12Gy), mean dose to normal brain, and maximum dose covering 0.03cc brainstem (D0.03cc). Plan complexity was also assessed via edge metric, modulation complexity score (MCS), mean MLC gap, mean MLC speed, and plan modulation (PM).

RESULTS

PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0 mm and large rotational errors ≥1.0° for either the couch or collimator. However, in some cases, errors close to or within generally accepted machine tolerances resulted in clinically relevant impacts. The greatest impact upon normal brain V12Gy, mean dose to normal brain, and D0.03cc brainstem was found for DLG error in alignment with other recent studies. All delivery errors had on average a minimal impact across these parameters. Comparing plans from the Monaco TPS and the Eclipse TPS, showed a lesser increase to V12Gy, mean dose to normal brain, and D0.03cc brainstem for Monaco plans (p < 0.01) when DLG error was simulated. Monaco plans also correlated to lower plan complexity. Using Spearman's correlation coefficient (r) a strong negative correlation (r ≤ -0.8) was found between the mean MLC gap and dose to OARs for DLG errors.

CONCLUSIONS

Reducing MLC complexity and using larger mean MLC gaps is recommended to improve plan robustness and reduce sensitivity to delivery and modelling errors. For cases in which the calculated dose distribution or dose indices are close to the clinically acceptable limits, this is especially important.

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

BACKGROUND

The 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.

PURPOSE

The 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.

METHODS

The 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.

RESULTS

Overall, 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.

CONCLUSIONS

ISDC 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.

Universal evaluation of MLC models in treatment planning systems based on a common set of dynamic tests

PURPOSE

To demonstrate the feasibility of characterising MLCs and MLC models implemented in TPSs using a common set of dynamic beams.

MATERIALS AND METHODS

A set of tests containing synchronous (SG) and asynchronous sweeping gaps (aSG) was distributed among twenty-five participating centres. Doses were measured with a Farmer-type ion chamber and computed in TPSs, which provided a dosimetric characterisation of the leaf tip, tongue-and-groove, and MLC transmission of each MLC, as well as an assessment of the MLC model in each TPS. Five MLC types and four TPSs were evaluated, covering the most frequent combinations used in radiotherapy departments.

RESULTS

Measured differences within each MLC type were minimal, while large differences were found between MLC models implemented in clinical TPSs. This resulted in some concerning discrepancies, especially for the HD120 and Agility MLCs, for which differences between measured and calculated doses for some MLC-TPS combinations exceeded 10%. These large differences were particularly evident for small gap sizes (5 and 10 mm), as well as for larger gaps in the presence of tongue-and-groove effects. A much better agreement was found for the Millennium120 and Halcyon MLCs, differences being within ± 5% and ± 2.5%, respectively.

CONCLUSIONS

The feasibility of using a common set of tests to assess MLC models in TPSs was demonstrated. Measurements within MLC types were very similar, but TPS dose calculations showed large variations. Standardisation of the MLC configuration in TPSs is necessary. The proposed procedure can be readily applied in radiotherapy departments and can be a valuable tool in IMRT and credentialing audits.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

OBJECTIVE

To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML).

METHODS

In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude.

RESULTS

The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1-96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively.

CONCLUSION

It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

Comparison of dosimetric effects of MLC positional errors on VMAT and IMRT plans for SBRT radiotherapy in non-small cell lung cancer

The positional accuracy of multi-leaf collimators (MLC) is important in stereotactic body radiotherapy (SBRT). The aim of this study was to investigate the impact between MLC positional error and dosimetry of volume intensity modulated (VMAT) and general intensity modulated (IMRT) plans for non-small cell lung cancer (NSCLC). Fifteen patients with NSCLC were selected to design the 360 SBRT-VMAT plans and the 360 SBRT-IMRT error plans. The DICOM files for these treatment plans were imported into a proprietary computer program that introduced delivery errors. Random and systematic MLC position (0.1, 0.2, 0.5, 1.0, 1.5, and 2.0 mm) errors were introduced. The systematic errors were shift errors (caused by gravity), opening errors, and closing errors. The CI, GI, d2cm and generalized equivalent uniform dose (gEUD) were calculated for the original plan and all treatment plans, accounting for the errors. Dose sensitivity was calculated using linear regression for MLC position errors. The random MLC errors were relatively insignificant. MLC shift, opening, and closing errors had a significant effect on the dose distribution of the SBRT plan. VMAT was more significant than IMRT. To ensure that the gEUD variation of PTV is controlled within 2%, the shift error, opening error, and closing error of IMRT should be less than 2.4 mm, 1.15 mm, and 0.97 mm, respectively. For VMAT, the shift error, opening error, and closing error should be less than 0.95 mm, 0.32 mm, and 0.38 mm, respectively. The dose sensitivity results obtained in this study can be used as a guide for patient-based quality assurance efforts. The position error of the MLC system had a significant impact on the gEUD of the SBRT technology. The MLC systematic error has a greater dosimetric impact on the VMAT plan than on the IMRT plan for SBRT, which should be carefully monitored.

Monte Carlo-based independent dose verification of radiosurgery HyperArc plans

PURPOSE

To investigate the feasibility of using the free PRIMO Monte Carlo software for independent dose check of cranial SRS plans designed with the Varian HyperArc (HA) technique.

MATERIALS AND METHODS

In this study, the PRIMO Monte Carlo software v. 0.3.64.1800 was used with the phase-space files (v. 2, Feb. 27, 2013) provided by Varian for 6 MV flattening-filter-free (FFF) photon beams from a Varian TrueBeam linear accelerator (linac), equipped with a Millennium 120 multileaf collimator (MLC). This configuration was validated by comparing the percentage depth doses (PDDs), lateral profiles and relative output factors (OFs) simulated in a water phantom against measurements for field sizes from 1 × 1 to 40 × 40 cm². The agreement between simulated and experimental relative dose curves was evaluated using a global (G) gamma index analysis. In addition, the accuracy of PRIMO to model the MLC was investigated (dosimetric leaf gap, tongue and groove, leaf transmission and interleaf leakage). Thirty-five HA SRS plans computed in the Eclipse treatment planning system (TPS) were simulated in PRIMO. The Acuros XB algorithm v. 16.10 (dose to medium) was used in Eclipse. Sixty targets with diameters ranging from 6 to 33 mm were included. Agreement between the dose distributions given by Eclipse and PRIMO was evaluated in terms of 3D global gamma passing rates (GPRs) for the 2 %/2 mm criteria.

RESULTS

Average GPR greater than 95 % with the 2 %(G)/1 mm criteria were obtained over the PDD and profiles of each field size. Differences between PRIMO calculated and measured OFs were within 0.5 % in all fields, except for the 1 × 1 cm² with a discrepancy of 1.5 %. Regarding the MLC modeling in PRIMO, an agreement within 3 % was achieved between calculated and experimental doses. Excellent agreement between PRIMO and Eclipse was found for the 35 HA plans. The 3D global GPRs (2 %/2 mm) for the targets and external patient contour were 99.6 % ± 1.1 % and 99.8 % ± 0.5 %, respectively.

CONCLUSIONS

According to the results described in this study, the PRIMO Monte Carlo software, in conjunction with the 6X FFF Varian phase-space files, can be used as secondary dose calculation software to check stereotactic radiosurgery plans from Eclipse using the HyperArc technique.

Is a weekly qualitative picket fence test sufficient? A proposed alternate EPID-based weekly MLC QA program

PURPOSE

Well-designed routine multileaf collimator (MLC) quality assurance (QA) is important to assure external-beam radiation treatment delivery accuracy. This study evaluates the clinical necessity of a comprehensive weekly (C-Weekly) MLC QA program compared to the American Association of Physics in Medicinerecommended weekly picket fence test (PF-Weekly), based on our seven-year experience with weekly MLC QA.

METHODS

The C-Weekly MLC QA program used in this study includes 5 tests to analyze: (1) absolute MLC leaf position; (2) interdigitation MLC leaf position; (3) picket fence MLC leaf positions at static gantry angle; (4) minimum leaf-gap setting; and (5) volumetric-modulated arc therapy delivery. A total of 20,226 QA images from 16,855 tests (3,371 tests × 5) for 11 linacs at 5 photon clinical sites from May 2014 to June 2021 were analyzed. Failure mode and effects analysis was performed with 5 failure modes related to the 5 tests. For each failure mode, a risk probability number (RPN) was calculated for a C-Weekly and a PF-Weekly MLC QA program. The probability of occurrence was evaluated from statistical analyses of the C-Weekly MLC QA.

RESULTS

The total number of failures for these 16,855 tests was 143 (0.9%): 39 (27.3%) for absolute MLC leaf position, 13 (9.1%) for interdigitation position, 9 (6.3%) for static gantry picket fence, 2 (1.4%) for minimum leaf-gap setting, and 80 (55.9%) for VMAT delivery. RPN scores for PF-Weekly MLC QA ranged from 60 to 192 and from 48 to 96 for C-Weekly MLC QA.

CONCLUSION

RPNs for the 5 failure modes of MLC QA tests were quantitatively determined and analyzed. A comprehensive weekly MLC QA is imperative to lower the RPNs of the 5 failure modes to the desired level (<125); those from the PF-Weekly MLC QA program were found to be higher (>125). This supports the clinical necessity for comprehensive weekly MLC QA.

Impact of systematic MLC positional uncertainties on the quality of single-isocenter multi-target VMAT-SRS treatment plans

Purpose

To study the impact of systematic MLC leaf positional uncertainties (stemming from mechanical inaccuracies or sub‐optimal MLC modeling) on the quality of intracranial single‐isocenter multi‐target VMAT‐SRS treatment plans. An estimation of appropriate tolerance levels is attempted.

Methods

Five patients, with three to four metastases and at least one target lying in close proximity to organs‐at‐risk (OARs) were included in this study. A single‐isocenter multi‐arc VMAT plan per patient was prepared, which served as the reference for dosimetric impact evaluation. A range of leaf offsets was introduced (±0.03 mm up to ±0.30 mm defined at the MLC plane) to both leaf banks, by varying the leaf offset MLC modeling parameter in Monaco for all the prepared plans, in order to simulate projected leaf offsets of ±0.09 mm up to ±0.94 mm at the isocenter plane, respectively. For all offsets simulated and cases studied, dose distributions were re‐calculated and compared with the corresponding reference ones. An experimental dosimetric procedure using the SRS mapCHECK diode array was also performed to support the simulation study results and investigate its suitability to detect small systematic leaf positional errors.

Results

Projected leaf offsets of ±0.09 mm were well‐tolerated with respect to both target dosimetry and OAR‐sparing. A linear relationship was found between D_95% percentage change and projected leaf offset (slope: 12%/mm). Impact of projected offset on target dosimetry was strongly associated with target volume. In two cases, plans that could be considered potentially clinically unacceptable (i.e., clinical dose constraint violation) were obtained even for projected offsets as small as 0.19 mm. The performed experimental dosimetry check can detect potential small systematic leaf errors.

Conclusions

Plan quality indices and dose–volume metrics are very sensitive to systematic sub‐millimeter leaf positional inaccuracies, projected at the isocenter plane. Acceptable and tolerance levels in systematic MLC uncertainties need to be tailored to VMAT‐SRS spatial and dosimetric accuracy requirements.

Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation

Purpose

Multi‐leaf‐collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is to test the sensitivity of machine log files in comparison to electronic portal imaging device (EPID)‐based measurements to MLC position errors caused by leaf backlash. The secondary aim is to investigate the effect of MLC leaf backlash on MLC leaf motion during clinical dynamic plan delivery.

Methods

The sensitivity of machine log files and two EPID‐based measurements were assessed via a controlled experiment, whereby the length of the “T” section of a series of 12 MLC leaf T‐nuts in a Varian Millennium MLC for a Trilogy C‐series type linac was reduced by sandpapering the top of the “T” to introduce backlash. The built‐in machine MLC leaf backlash test as well as measurements for two EPID‐based dynamic MLC positional tests along with log files were recorded pre‐ and post‐T‐nut modification. All methods were investigated for sensitivity to the T‐nut change by assessing the effect on measured MLC leaf positions. A reduced version of the experiment was repeated on a TrueBeam type linac with Millennium MLC.

Results

No significant differences before and after T‐nut modification were detected in any of the log file data. Both EPID methods demonstrated sensitivity to the introduced change at approximately the expected magnitude with a strong dependence observed with gantry angle. EPID‐based data showed MLC positional error in agreement with the micrometer measured T‐nut length change to 0.07 ± 0.05 mm (1 SD) using the departmental routine QA test. Backlash results were consistent between linac types.

Conclusion

Machine log files appear insensitive to MLC position errors caused by MLC leaf backlash introduced via the T‐nut. The effect of backlash on clinical MLC motions is heavily gantry angle dependent.

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

PURPOSE

Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans.

METHODS

For this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R² , and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance.

RESULTS

The models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045).

CONCLUSIONS

We examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

Effect of plan complexity on the dosimetry, delivery accuracy, and interplay effect in lung VMAT SBRT with 6 MV FFF beam

PURPOSE

The purpose of this study is to investigate the effect of plan complexity on the dosimetry, delivery accuracy, and interplay effect in lung stereotactic body radiation therapy (SBRT) using volumetric modulated arc therapy (VMAT) with 6 MV flattening-filter-free (FFF) beam.

METHODS

Twenty patients with early stage non-small cell lung cancer were included. For each patient, high-complexity (HC) and low-complexity (LC) three-partial-arc VMAT plans were optimized by adjusting the normal tissue objectives and the maximum monitoring units (MUs) for a Varian TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA, USA) using 6 MV FFF beam. The effect of plan complexity was comprehensively evaluated in three aspects: (1) The dosimetric parameters, including CI, D_2cm, R₅₀, and dose-volume parameters of organs at risk were compared. (2) The delivery accuracy was assessed by pretreatment quality assurance for two groups of plans. (3) The motion-induced dose deviation was evaluated based on point dose measurements near the tumor center by using a programmable phantom. The standard deviation (SD) and maximum dose difference of five measurements were used to quantify the interplay effect.

RESULTS

The dosimetry of HC and LC plans were similar except the CI (1.003 ± 0.032 and 1.026 ± 0.043, p = 0.030) and D_max to the spinal cord (10.6 ± 3.2 and 9.9 ± 3.0, p = 0.012). The gamma passing rates were significantly higher in LC plans for all arcs (p < 0.001). The SDs of HC and LC plans ranged from 0.5-16.6% and 0.03-2.9%, respectively, under the conditions of one-field, two-field, and three-field delivery for each plan with 0.5, 1, 2, and 3 cm motion amplitudes. The maximum dose differences of HC and LC plans were 34.5% and 9.1%, respectively.

CONCLUSION

For lung VMAT SBRT, LC plans have a higher delivery accuracy and a lower motion-induced dose deviation with similar dosimetry compared with HC plans.

Correlation Between Average Segment Width and Gamma Passing Rate as a Function of MLC Position Error in Volumetric Modulated Arc Therapy

Objective:

This study analyzed the correlation between the average segment width (ASW) and gamma passing rate according to the multi-leaf collimator (MLC) position error.

Method:

To evaluate the changes in the gamma passing rate according to the MLC position error, 21 volumetric modulated arc therapy (VMAT) plans were generated using pelvic lymph node metastatic prostate cancer patient's data which is sensitive to MLC position errors as they involve several long, narrow, irregular fields. The ASW for each VMAT plan was calculated using our own code developed using Visual Basic for Applications (VBA). The gamma passing rate of the VMAT plan according to the MLC position error was evaluated using ArcCHECK (Sun Nuclear, Melbourne, FL, USA) while inducing symmetric MLC position errors in 0.25 mm intervals from −1 mm to +1 mm in the infinity medical linear accelerator (Elekta AB, Stockholm, Sweden). Finally, we examined the correlation between the change in the passing rate (γgradient) due to the MLC position error and the ASW in VMAT through linear regression analysis using the least squares method.

Results:

The ASW and γgradient were found to have a linear correlation according to the MLC position error, and the coefficient of determination was 0.88. For a 1 mm position error of MLC in VMAT, the gamma passing rate improved by approximately 11.9% as the ASW increased by 10 mm.

Conclusion:

These results are expected to be employed as guidelines to minimize the dose uncertainty due to MLC position error in VMAT.

Predicted probabilities of brain injury after carbon ion radiotherapy for head and neck and skull base tumors in long-term survivors

BACKGROUND AND PURPOSE

We aimed to determine the risk factors for radiation-induced brain injury (RIBI¹) after carbon ion radiotherapy (CIRT) to predict their probabilities in long-term survivors.

MATERIALS AND METHODS

We evaluated 104 patients with head, neck, and skull base tumors who underwent CIRT in a regimen of 32 fractions and were followed up for at least 24 months. RIBI was assessed using the Common Terminology Criteria for Adverse Events.

RESULTS

The median follow-up period was 45.5 months; 19 (18.3 %) patients developed grade ≥2 RIBI. The maximal absolute dose covering 5 mL of the brain (D5ml) was the only significant risk factor for grade ≥2 RIBI in the multivariate logistic regression analysis (p = 0.001). The tolerance doses of D5ml for the 5% and 50% probabilities of developing grade ≥2 RIBI were estimated to be 55.4 Gy (relative biological effectiveness [RBE]) and 68.4 Gy (RBE) by a logistic model, respectively.

CONCLUSION

D5ml was most significantly associated with grade ≥2 RIBI and may enable the prediction of its probability.

Define dose field to assess the modulation complexity of intensity-modulated radiation therapy

PURPOSE

Introduce a new concept of dose field to assess the modulation complexity (MC) of intensity-modulated radiation therapy (IMRT).

METHODS

A total of 91 IMRT plans for different diseases were retrospectively retrieved randomly from treatment database. The dose field of plans were calculated and feature values such as force magnitude and diversity were defined and extracted. Correlation analysis between these feature values and execution cost, delivery accuracy of plans was performed, to verify the validity of dose field in characterizing the MC.

RESULTS

The feature values of dose field in different disease own significant differences (p < 0.001). For correlation analysis, number of control point (CP) and cumulative perimeter of CP have the highest correlation with angle entropy (0.815 and 0.848 respectively), while the correlation between number of monitor units(MU), cumulative area of CP and force, force entropy is higher than others (0.797-0.909). However, complexity of CP shape is almost irrelevant to all the dose field features. The gamma passing rate and the dose field features shows a weak negative correlation trend.

CONCLUSIONS

Dose field can be used as a tool to assess the MC of IMRT.

Usability of detecting delivery errors during treatment of prostate VMAT with a gantry-mounted transmission detector

Volumetric‐modulated arc therapy (VMAT) requires highly accurate control of multileaf collimator (MLC) movement, rotation speed of linear accelerator gantry, and monitor units during irradiation. Pretreatment validation and monitoring of these factors during irradiation are necessary for appropriate VMAT treatment. Recently, a gantry mounted transmission detector “Delta⁴ Discover® (D4D)” was developed to detect errors in delivering doses and dose distribution immediately after treatment. In this study, the performance of D4D was evaluated. Simulation plans, in which the MLC position was displaced by 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm from the clinically used original plans, were created for ten patients who received VMAT treatment for prostate cancer. Dose deviation (DD), distance‐to‐agreement (DTA), and gamma index analysis (GA) for each plan were evaluated by D4D. These results were compared to the results (DD, DTA and GA) measured by Delta⁴ Phantom + (D4P). We compared the deviations between the planned and measured values of the MLC stop positions A‐side and B‐side in five clinical cases of prostate VMAT during treatment and measured the GA values. For D4D, when the acceptable errors for DD, DTA, and GA were determined to be ≤3%, ≤2 mm, and ≤3%/2 mm, respectively, the minimum detectable errors in the MLC position were 2.0, 1.5, and 1.5 mm based on DD, DTA, and GA respectively. The corresponding minimum detectable MLC position errors were 2.0, 1.0, and 1.5 mm, respectively, for D4P. The deviation between the planned and measured position of MLC stopping point of prostate VMAT during treatment was stable at an average of −0.09 ± 0.05 mm, and all GA values were above 99.86%. In terms of delivering doses and dose distribution of VMAT, error detectability of D4D was comparable to that of D4P. The transmission‐type detector “D4D” is thus suitable for detecting delivery errors during irradiation.

Identification of a potential source of error for 6FFF beams delivered on an AgilityTM multileaf collimator

Purpose

The performance of the Agility^TM multileaf collimator was investigated with a focus on dynamic, small fields for flattening filter free (FFF) beams.

Methods

In this study we have developed a simple tool to test the robustness of the control mechanisms during dynamic beam delivery for Elekta’s VersaHD linear accelerator with Integrity 4.0.4 control software. We have programed the planning system to calculate dose for delivery of sweeping gaps. These sweeping gaps have a constant speed, constant size, and are delivered at a constant dose rate. Therefore they specifically identify delivery problems in dynamic mode.

Results

The Elekta Agility^TM control mechanism fails to maintain accurate delivery for small, dynamic sweeping gaps. For small gap sizes, the Agility^TM control mechanism delivers a field that is more than four times the size of the planned field width without generating an interlock. This has dosimetric implications: The discrepancy between calculated and measured doses increases with decreasing gap size and exceeds 10% and 60% at isocenter for a 3.5 mm and 1 mm gap size, respectively.

Conclusion

A deficiency of the Agility^TM control system was identified in this study. This deficiency is a potential source of error for volumetric modulated arc therapy fields and could therefore contribute to relatively high failure rates in quality assurance measurements, especially for FFF beams.

Effect of Multileaf Collimator Leaf Position Error Determined by Picket Fence Test on Gamma Index Value in Patient-Specific Quality Assurance of Volumetric-Modulated Arc Therapy Plans

Aim The correlation between the MLC QA (IBA Dosimetry, Germany) results of the picket fence test created with intentional errors and the patient's quality assurance (QA) evaluation was investigated to assess the impact of multileaf collimator (MLC) positioning error on patient QA. Materials and methods The picket fence, including error-free and intentional MLC errors, defined in Bank In, Bank Out, and Bank Both were analyzed using MLC QA. The QA of 15 plans consisting of stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and conventionally fractionated volumetric-modulated arc therapy (VMAT) acquired with electronic portal imaging devices (EPID) was evaluated in the presence of error-free and MLC errors. The QA of plans were analyzed with 2%/2 mm and 3%/3 mm criteria. Results The passing rates of the picket fence test were 97%, 92%, 91%, and 87% for error-free and intentional errors. The criterion of 3%/3 mm wasn't able to detect an MLC error for either SRS/SBRT or conventionally fractionated VMAT. The criterion of 2%/2mm was more sensitive to detect MLC error for the conventionally fractionated VMAT than SRS/SBRT. While only two of SBRT plans had <90%, four of conventionally fractionated VMAT plans had a <90% passing rate. Conclusion We found that the systematic MLC positioning errors defined with picket fence have a smaller but measurable impact on SRS/SBRT than the VMAT plan for a conventionally fractionated and relatively complex plan such as head and neck and endometrium cases.

Trajectory log analysis and cone-beam CT-based daily dose calculation to investigate the dosimetric accuracy of intensity-modulated radiotherapy for gynecologic cancer

This study evaluated unexpected dosimetric errors caused by machine control accuracy, patient setup errors, and patient weight changes/internal organ deformations. Trajectory log files for 13 gynecologic plans with seven‐ or nine‐beam dynamic multileaf collimator (MLC) intensity‐modulated radiation therapy (IMRT), and differences between expected and actual MLC positions and MUs were evaluated. Effects of patient setup errors on dosimetry were estimated by in‐house software. To simulate residual patient setup errors after image‐guided patient repositioning, planned dose distributions were recalculated (blurred dose) after the positions were randomly moved in three dimensions 0–2 mm (translation) and 0°–2° (rotation) 28 times per patient. Differences between planned and blurred doses in the clinical target volume (CTV) D_98% and D_2% were evaluated. Daily delivered doses were calculated from cone‐beam computed tomography by the Hounsfield unit‐to‐density conversion method. Fractional and accumulated dose differences between original plans and actual delivery were evaluated by CTV D_98% and D_2%. The significance of accumulated doses was tested by the paired t test. Trajectory log file analysis showed that MLC positional errors were −0.01 ± 0.02 mm and MU delivery errors were 0.10 ± 0.10 MU. Differences in CTV D_98% and D_2% were <0.5% for simulated patient setup errors. Differences in CTV D_98% and D_2% were 2.4% or less between the fractional planned and delivered doses, but were 1.7% or less for the accumulated dose. Dosimetric errors were primarily caused by patient weight changes and internal organ deformation in gynecologic radiation therapy.

An error detection method for real-time EPID-based treatment delivery quality assurance

PURPOSE

To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real-time EPID images.

METHODS

The on-board EPID imager was used to measure cine-EPID (~10 Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical plans. For each frame acquisition, planned apertures were interpolated from the treatment plan multileaf collimator (MLC) positions expected during the frame acquisition interval. Inaccurate deliveries were identified by monitoring in-aperture missed fluence and out-of-aperture excess fluence beyond a specified buffer. Delivery errors were simulated by perturbing the planned MLC positions before comparison with nonperturbed measured apertures. Systematic 1-5 mm MLC leaf shifts were used to train a logistic regression model to determine the error detection threshold. Model accuracy was monitored using tenfold cross-validation. The model's error detection ability was tested with other error modes: plan control point (CP) weight perturbations, collimator rotations, random MLC leaf position errors, EPID imager shift, and stuck MLC leaf. The error detection accuracy was evaluated using the Matthews correlation coefficient (MCC) and the false positive rate (FPR). Per-beam error thresholds of >1, >5, and >10% errant frames were tested to label per-beam errors. The model also was tested for its ability to distinguish five cases with highly similar plans and compared with gamma analysis.

RESULTS

Delivery errors were detected by monitoring intended per-frame images with a 2 mm MLC buffer. Frame-by-frame aperture errors were identified with an optimal threshold of 0.3% of the expected aperture area. The per-frame FPR was 0.02%. The MCC was 1.00 (perfect classification) for detection based on 1% of frames for random CP weight shift, 3 mm random MLC shifts, 90° and 180° collimator rotations, and an MLC leaf stuck after 10% of the beam delivery. The MCC for 2°, 4°, and 8° collimator rotation were 0.53, 0.76, and 0.96, respectively, for the 1% of beam delivery threshold. The 3 mm EPID shift had poor detection, with a minimum MCC of 0.14. The highly similar plans were reliably detected by the aperture check but were not detectable with gamma analysis.

CONCLUSION

The high error detection sensitivity and low FPR makes the aperture check error detection method well suited to pretreatment and during-treatment beam delivery quality assurance (QA). The aperture check detects subtle beam delivery errors, including those resulting from MLC leaf positioning deviations, CP MU shifts, and stuck MLC leaves. Furthermore, the method can distinguish between highly similar treatment plans. Since the aperture check method monitors for the aperture shapes over a given MU interval, it is also sensitive to errors in MU per CP, without requiring dosimetric calibration of the EPID. The aperture check is one part of a Swiss cheese error detection scheme, which provides redundant error testing of multiple error modes, including nonaperture related errors. The rapid error detection, at 1% of a beam's delivery, make the aperture check a potential candidate for QA of on-line adaptive radiotherapy, or other situations in which pretreatment delivery QA is impractical.

The structural similarity index for IMRT quality assurance: radiomics-based error classification

PURPOSE

The implementation of radiomics and machine learning (ML) techniques on analyzing two-dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub-index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub-index maps and develop ML models to classify delivery errors in patient-specific dynamic IMRT QA.

METHODS

Twenty-one sliding-window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine-related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same-directional and opposite-directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub-index maps, and one dose difference-derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross-validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error-free and the erroneous cases was compared to that of the conventional gamma analysis.

RESULTS

The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear-SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error-free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model.

CONCLUSIONS

We proposed an ML-based method for machine-related error identification in patient-specific dynamic IMRT QA, where radiomic analysis on SSIM sub-index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error identification for the patient-specific IMRT QA process.

Effect of MU-weighted multi-leaf collimator position error on dose distribution of SBRT radiotherapy in peripheral non-small cell lung cancer

PURPOSE

Position accuracy of the multi-leaf collimator (MLC) is essential in stereotactic body radiotherapy (SBRT). This study is aimed to investigate the dosimetric impacts of the MU-weighted MLC positioning uncertainties of SBRT for patients with early stage peripheral non-small cell lung cancer (NSCLC).

METHODS

Three types of MLC position error were simulated: Type 1, random error; Type 2, system shift, in which both MLC banks shifted to the left or right direction; and Type 3, in which both MLC banks moved with same magnitudes in the opposite directions. Two baseline plans were generated: an automatic plan (AP) and a manually optimized plan (MP). Multi-leaf collimator position errors were introduced to generate simulated plans with the preset MLC leaf position errors, which were then reimported into the Pinnacle system to generate simulated plans, respectively. The dosimetric parameters (CI, nCI, GI, etc.) and gEUD values of PTV and OARs were calculated. Linear regression between MU-weighted/unweighted MLC position error and gEUD was performed to obtain dose sensitivity.

RESULTS

The dose sensitivities of the PTVs were -4.93, -38.94, -41.70, -55.55, and 30.33 Gy/mm for random, left shift, right shift, system close, and system open MLC errors, respectively. There were significant differences between the MU-weighted and the unweighted dose sensitivity, which was -38.94 Gy/mm vs -3.42 Gy/mm (left shift), -41.70 Gy/mm vs -3.56 Gy/mm (right shift), -55.55 Gy/mm vs -4.84 Gy/mm (system close), and 30.33 vs 2.64 Gy/mm (system open). For the system open/close MLC errors, as the PTV volume became larger, the dose sensitivity decreased. APs provided smaller dose sensitivity for the system shift and system close MLC errors compared to the conventional MPs.

CONCLUSIONS

There was significant difference in dose sensitivity between MU-weighted and unweighted MLC position error of SBRT radiotherapy in peripheral NSCLC. MU is suggested to be included in the dosimetric evaluation of the MLC misalignments, since it is much closer to clinical radiotherapy.

Sensitivity of IROC phantom performance to radiotherapy treatment planning system beam modeling parameters based on community-driven data

PURPOSE

Treatment planning system (TPS) dose calculations have previously been shown to be sensitive to modeling errors, especially when treating with complex strategies like intensity-modulated radiation therapy (IMRT). This work investigates the dosimetric impact of several dosimetric and nondosimetric beam modeling parameters, based on their distribution in the radiotherapy community, in two commercial TPSs in order to understand the realistic potential for dose deviations and their clinical effects.

METHODS AND MATERIALS

Beam models representing standard 120-leaf Varian Clinac-type machines were developed in Eclipse 13.5 (AAA algorithm) and RayStation 9A (v8.99, collapsed-cone algorithm) based upon median values of dosimetric measurements from Imaging and Radiation Oncology Core (IROC) Houston site visit data and community beam modeling parameter survey data in order to represent a baseline linear accelerator. Five clinically acceptable treatment plans (three IMRT, two VMAT) were developed for the IROC head and neck phantom. Dose distributions for each plan were recalculated after individually modifying parameters of interest (e.g., MLC transmission, percent depth doses [PDDs], and output factors) according to the 2.5th to 97.5th percentiles of community survey and machine performance data to encompass the realistic extent of variance in the radiotherapy community. The resultant dose distributions were evaluated by examining relative changes in average dose for thermoluminescent dosimeter (TLD) locations across the two target volumes and organ at risk (OAR). Interplay was also examined for parameters generating changes in target dose greater than 1%.

RESULTS

For Eclipse, dose calculations were sensitive to changes in the dosimetric leaf gap (DLG), which resulted in differences from -5% to +3% to the targets relative to the baseline beam model. Modifying the MLC transmission factor introduced differences up to ± 1%. For RayStation, parameters determining MLC behaviors likewise contributed substantially; the MLC offset introduced changes in dose from -4% to +7%, and the MLC transmission caused changes of -4% to +2%. Among the dosimetric qualities examined, changes in PDD implementation resulted in the most substantial changes, but these were only up to ±1%. Other dosimetric factors had <1% impact on dose accuracy. Interplay between impactful parameters was found to be minimal.

CONCLUSION

Factors related to the modeling of the MLC, particularly relating to the leaf offset, can cause clinically significant changes in the calculated dose for IMRT and VMAT plans. This should be of concern to the radiotherapy community because the clinical effects of poor TPS commissioning were based on reported data from clinically implemented beam models. These results further reinforce that dose errors caused by poor TPS calculations are often involved in IROC phantom failures.

The impact of isocentric shifts on delivery accuracy during the irradiation of small cerebral targets-Quantification and possible corrections

Purpose

To assess the impact of isocenter shifts due to linac gantry and table rotation during cranial stereotactic radiosurgery on D₉₈, target volume coverage (TVC), conformity (CI), and gradient index (GI).

Methods

Winston‐Lutz (WL) checks were performed on two Elekta Synergy linacs. A stereotactic quality assurance (QA) plan was applied to the ArcCHECK phantom to assess the impact of isocenter shift corrections on Gamma pass rates. These corrections included gantry sag, distance of collimator and couch axes to the gantry axis, and distance between cone‐beam computed tomography (CBCT) isocenter and treatment beam (MV) isocenter. We applied the shifts via script to the treatment plan in Pinnacle 16.2. In a planning study, isocenter and mechanical rotation axis shifts of 0.25 to 2 mm were applied to stereotactic plans of spherical planning target volumes (PTVs) of various volumes. The shifts determined via WL measurements were applied to 16 patient plans with PTV sizes between 0.22 and 10.4 cm³.

Results

ArcCHECK measurements of a stereotactic treatment showed significant increases in Gamma pass rate for all three measurements (up to 3.8 percentage points) after correction of measured isocenter deviations. For spherical targets of 1 cm³, CI was most severely affected by increasing the distance of the CBCT isocenter (1.22 to 1.62). Gradient index increased with an isocenter‐collimator axis distance of 1.5 mm (3.84 vs 4.62). D₉₈ (normalized to reference) dropped to 0.85 (CBCT), 0.92 (table axis), 0.95 (collimator axis), and 0.98 (gantry sag), with similar but smaller changes for larger targets. Applying measured shifts to patient plans lead to relevant drops in D₉₈ and TVC (7%) for targets below 2 cm³ treated on linac 1.

Conclusion

Mechanical deviations during gantry, collimator, and table rotation may adversely affect the treatment of small stereotactic lesions. Adjustments of beam isocenters in the treatment planning system (TPS) can be used to both quantify their impact and for prospective correction of treatment plans.

Impact of the MLC leaf-tip model in a commercial TPS: Dose calculation limitations and IROC-H phantom failures

Purpose

Treatment planning system (TPS) dose calculation is sensitive to multileaf collimator (MLC) modeling, especially when treating with intensity‐modulated radiation therapy (IMRT) or VMAT. This study investigates the dosimetric impact of the MLC leaf‐tip model in a commercial TPS (RayStation v.6.1). The detectability of modeling errors was assessed through both measurements with an anthropomorphic head‐and‐neck phantom and patient‐specific IMRT QA using a 3D diode array.

Methods and Materials

An Agility MLC (Elekta Inc.) was commissioned in RayStation. Nine IMRT and VMAT plans were optimized to treat the head‐and‐neck phantom from the Imaging and Radiation Oncology Core Houston branch (IROC‐H). Dose distributions for each plan were re‐calculated on 27 beam models, varying leaf‐tip width (2.0, 4.5, and 6.5 mm) and leaf‐tip offset (−2.0 to +2.0 mm) values. Doses were compared to phantom TLD measurements. Patient‐specific IMRT QA was performed, and receiver‐operating characteristic (ROC) analysis was performed to determine the detectability of modeling errors.

Results

Dose calculations were very sensitive to leaf‐tip offset values. Offsets of ±1.0 mm resulted in dose differences up to 10% and 15% in the PTV and spinal cord TLDs respectively. Offsets of ±2.0 mm caused dose deviations up to 50% in the spinal cord TLD. Patient‐specific IMRT QA could not reliably detect these deviations, with an ROC area under the curve (AUC) value of 0.537 for a ±1.0 mm change in leaf‐tip offset, corresponding to >7% dose deviation. Leaf‐tip width had a modest dosimetric impact with <2% and 5.6% differences in the PTV and spinal cord TLDs respectively.

Conclusions

Small changes in the MLC leaf‐tip offset in this TPS model can cause large changes in the calculated dose for IMRT and VMAT plans that are difficult to identify through either dose curves or standard patient‐specific IMRT QA. These results may, in part, explain the reported high failure rate of IROC‐H phantom tests.

Assessing the dependency of the uncertainties in the Elekta Agility MLC calibration procedure on the focal spot position

The effectiveness of radiotherapy treatments depends on the accuracy of the dose delivery process. The majority of radiotherapy courses are delivered on linear accelerators with a Multi Leaf Collimator (MLC) in 3D conformal Radiation Therapy, Intensity Modulated Radiation Therapy (IMRT) or Volumetric Modulated Arc Therapy (VMAT) modes that require accurate MLC positioning. This study investigates the MLC calibration accuracy, following manufacturer procedures for an Elekta Synergy linac with the Agility head, against the radiation focal spot offset (alignment with the collimator axis of rotation). If the radiation focal spot is not aligned ideally with the collimator axis of rotation then a systematic error can be introduced into the calibration procedure affecting absolute MLC leaf positions. Calibration of diaphrams is equally affected; however they are not investigated here. The results indicate that an estimated 0.15 mm MLC uncertainty in all MLC leaves positions can be introduced due to uncertainty of the radiation focal spot position of 0.21 mm.

Dosimetric impact and detectability of multi-leaf collimator positioning errors on Varian Halcyon

The purpose of this study is to investigate the dosimetric impact of multi‐leaf collimator (MLC) positioning errors on a Varian Halcyon for both random and systematic errors, and to evaluate the effectiveness of portal dosimetry quality assurance in catching clinically significant changes caused by these errors. Both random and systematic errors were purposely added to 11 physician‐approved head and neck volumetric modulated arc therapy (VMAT) treatment plans, yielding a total of 99 unique plans. Plans were then delivered on a preclinical Varian Halcyon linear accelerator and the fluence was captured by an opposed portal dosimeter. When comparing dose–volume histogram (DVH) values of plans with introduced MLC errors to known good plans, clinically significant changes to target structures quickly emerged for plans with systematic errors, while random errors caused less change. For both error types, the magnitude of clinically significant changes increased as error size increased. Portal dosimetry was able to detect all systematic errors, while random errors of ±5 mm or less were unlikely to be detected. Best detection of clinically significant errors, while minimizing false positives, was achieved by following the recommendations of AAPM TG‐218. Furthermore, high‐ to moderate correlation was found between dose DVH metrics for normal tissues surrounding the target and portal dosimetry pass rates. Therefore, it may be concluded that portal dosimetry on the Halcyon is robust enough to detect errors in MLC positioning before they introduce clinically significant changes to VMAT treatment plans.

Quality assurance of linear accelerator: a comprehensive system using electronic portal imaging device

Aim

The Electronic Portal Imaging Device (EPID), primarily used for patient setup during radiotherapy sessions is also used for dosimetric measurements. In the present study, the feasibility of EPID in both machine and patient-specific quality assurance (QA) are investigated. We have developed a comprehensive software tool for effective utilisation of EPID in our institutional QA protocol.

Materials and methods

Portal Vision aS1000, amorphous silicon portal detector attached to Clinac iX—Linear Accelerator (LINAC) was used to measure daily profile and output constancy, various Multi-Leaf Collimator (MLC) checks and patient plan verification. Different QA plans were generated with the help of Eclipse Treatment Planning System (TPS) and MLC shaper software. The indigenously developed MATLAB programs were used for image analysis. Flatness, symmetry, output constancy, Field Width at Half Maximum (FWHM) and fluence comparison were studied from images obtained from TPS and EPID dosimetry.

Results

The 3 years institutional data of profile constancy and patient-specific QA measured using EPID were found within the acceptable limits. The daily output of photon beam correlated with the output obtained through solid phantom measurements. The Pearson correlation coefficients are 0.941 (p = 0.0001), 0.888 (p = 0.0188) and 0.917 (p = 0.0007) for the years of 2014, 2015 and 2016, respectively. The accuracy of MLC for shaping complex treatment fields was studied in terms of FWHM at different portions of various fields, showed good agreement between TPS-generated and EPID-measured MLC positions. The comparison of selected patient plans in EPID with an independent 2D array detector system showed statistically significant correlation between these two systems. Percentage difference between TPS computed and EPID measured fluence maps calculated for number of patients using MATLAB code also exhibited the validity of those plans for treatment.

Highly efficient and sensitive patient-specific quality assurance for spot-scanned proton therapy

The purpose of this work was to develop an end-to-end patient-specific quality assurance (QA) technique for spot-scanned proton therapy that is more sensitive and efficient than traditional approaches. The patient-specific methodology relies on independently verifying the accuracy of the delivered proton fluence and the dose calculation in the heterogeneous patient volume. A Monte Carlo dose calculation engine, which was developed in-house, recalculates a planned dose distribution on the patient CT data set to verify the dose distribution represented by the treatment planning system. The plan is then delivered in a pre-treatment setting and logs of spot position and dose monitors, which are integrated into the treatment nozzle, are recorded. A computational routine compares the delivery log to the DICOM spot map used by the Monte Carlo calculation to ensure that the delivered parameters at the machine match the calculated plan. Measurements of dose planes using independent detector arrays, which historically are the standard approach to patient-specific QA, are not performed for every patient. The nozzle-integrated detectors are rigorously validated using independent detectors in regular QA intervals. The measured data are compared to the expected delivery patterns. The dose monitor reading deviations are reported in a histogram, while the spot position discrepancies are plotted vs. spot number to facilitate independent analysis of both random and systematic deviations. Action thresholds are linked to accuracy of the commissioned delivery system. Even when plan delivery is acceptable, the Monte Carlo second check system has identified dose calculation issues which would not have been illuminated using traditional, phantom-based measurement techniques. The efficiency and sensitivity of our patient-specific QA program has been improved by implementing a procedure which independently verifies patient dose calculation accuracy and plan delivery fidelity. Such an approach to QA requires holistic integration and maintenance of patient-specific and patient-independent QA.

Evaluating the dosimetric consequences of MLC leaf positioning errors in dynamic IMRT treatments

Purpose

The purpose of this study was to evaluate the dosimetric impact of multileaf collimator (MLC) positional errors on dynamic intensity-modulated radiotherapy (IMRT) treatments through planning simulation. Secondly the sensitivity of IMRT MatriXX device for detecting the MLC leaf positional errors was also evaluated.

Materials and methods

In this study five dynamic IMRT plans, each for brain and head–neck (HN), were retrospectively included. An in-house software was used to introduce random errors (uniform distribution between −2·0 and +2·0 mm) and systematic errors [±0·5, ±0·75, ±1·0 and ±2·0 mm (+: open MLC error and −: close MLC error)]. The error-introduced MLC files were imported into the treatment planning system and new dose distributions were calculated. Furthermore, the dose–volume histogram files of all plans were exported to in-house software for equivalent uniform dose (EUD), tumour control probability and normal tissue complication probability calculations. The error-introduced plans were also delivered on LINAC, and the planar fluences were measured by IMRT MatriXX. Further, 3%/3 mm and 2%/2 mm γ-criteria were used for analysis.

Results

In planning simulation study, the impact of random errors was negligible and ΔEUD was &lt;0·5±0·7%, for both brain and HN. The impact of systematic errors was substantial, and on average, the maximum change in EUD for systematic errors (close 2 mm) was −10·7±3·1% for brain and −15·5±2·6% for HN.

Conclusions

It can be concluded that the acceptable systematic error was 0·4 mm for brain and 0·3 mm for HN. Furthermore, IMRT MatriXX device was able to detect the MLC errors ≥2 mm in HN and &gt;3 mm errors in brain with 2%/2 mm γ-criteria.

A simple method for determining dosimetric leaf gap with cross-field dose width for rounded leaf-end multileaf collimator systems

PURPOSE

The dosimetric leaf gap (DLG) and multileaf collimator (MLC) transmission are two important systematic parameters used to model the rounded MLC leaf ends effect when commissioning an Eclipse treatment planning system (TPS). Determining the optimal DLG is a time consuming process. This study develops a simple and reliable method for determining the DLG using the cross-field dose width.

METHODS AND MATERIALS

A Varian TrueBeam linac with 6 MV, 10 MV, 6 MV flattening filter free (FFF) and 10 MV FFF photon beams and equipped with the 120 Millennium MLC and the Eclipse™ TPS was used in this study. Integral sliding fields and static slit MLC field doses with different gap widths were measured with an ionization chamber and GAFCHROMIC EBT3 films, respectively. Measurements were performed for different beam energies and at depths of 5 and 10 cm. DLGs were derived from a linear extrapolation to zero dose and intercepting at the gap width axis. In the ion chamber measurements method, the average MLC leaf transmission to the gap reading for each gap (RgT) were calculated with nominal and cross-field dose widths, respectively. The cross-field dose widths were determined according to the dose profile measured with EBT3 films. Additionally, the optimal DLG values were determined using plan dose measurements, as the value that produced the closest agreement between the planned and measured doses. DLGs derived from the nominal and cross-field dose width, the film measurements, and the optimal process, were obtained and compared.

RESULTS

The DLG values are insensitive to the variations in depth (within 0.07 mm). DLGs derived from nominal gap widths showed a significantly lower values (with difference about 0.5 mm) than that from cross-field dose widths and from film measurements and from plan optimal values. The method in deriving DLGs by correcting the nominal gap widths to the cross-field dose widths has shown good agreements to the plan optimal values (with difference within 0.21 mm).

CONCLUSIONS

The DLG values derived from the cross-field dose width method were consistent with the values derived from film measurements and from the plan optimal process. A simple and reliable method to determine DLG for rounded leaf-end MLC systems was established. This method provides a referable DLG value required during TPS commissioning.

Patient-Specific Quality Assurance Using Monte Carlo Dose Calculation and Elekta Log Files for Prostate Volumetric-Modulated Arc Therapy

Log file–based methods are attracting increasing interest owing to their ability to validate volumetric-modulated arc therapy outputs with high resolution in the leaf and gantry positions and in delivered dose. Cross-validation of these methods for comparison with measurement-based methods using the ionization chamber/ArcCHECK-3DVH software (version 3.2.0) under the same conditions of treatment anatomy and plan enables an efficient evaluation of this method. In this study, with the purpose of cross-validation, we evaluate the accuracy of a log file–based method using Elekta log files and an X-ray voxel Monte Carlo dose calculation technique in the case of leaf misalignment during prostate volumetric-modulated arc therapy. In this study, 10 prostate volumetric-modulated arc therapy plans were used. Systematic multileaf collimator leaf positional errors (±0.4 and ±0.8 mm for each single bank) were deliberately introduced into the optimized plans. Then, the delivered 3-dimensional doses to a phantom with a certain patient anatomy were estimated by our system. These doses were compared with the ionization chamber dose and the ArcCHECK-3DVH dose. For the given phantom and patient anatomy, the estimated dose strongly coincided with the ionization chamber/ArcCHECK-3DVH dose (P < .01). In addition, good agreement between the estimated dose and the ionization chamber/ArcCHECK-3DVH dose was observed. The dose estimation accuracy of our system, which combines Elekta log files and X-ray voxel Monte Carlo dose calculation, was evaluated.

A quantitative method to the analysis of MLC leaf position and speed based on EPID and EBT3 film for dynamic IMRT treatment with different types of MLC

A quantitative method based on the electronic portal imaging system (EPID) and film was developed for MLC position and speed testing; this method was used for three MLC types (Millennium, MLCi, and Agility MLC). To determine the leaf position, a picket fence designed by the dynamic (DMLC) model was used. The full‐width half‐maximum (FWHM) values of each gap measured by EPID and EBT3 were converted to the gap width using the FWHM versus nominal gap width relationship. The algorithm developed for the picket fence analysis was able to quantify the gap width, the distance between gaps, and each individual leaf position. To determine the leaf speed, a 0.5 × 20 cm²MLC‐defined sliding gap was applied across a 14 × 20 cm² symmetry field. The linacs ran at a fixed‐dose rate. The use of different monitor units (MUs) for this test led to different leaf speeds. The effect of leaf transmission was considered in a speed accuracy analysis. The difference between the EPID and film results for the MLC position is less than 0.1 mm. For the three MLC types, twice the standard deviation (2 SD) is provided; 0.2, 0.4, and 0.4 mm for gap widths of three MLC types, and 0.1, 0.2, and 0.2 mm for distances between gaps. The individual leaf positions deviate from the preset positions within 0.1 mm. The variations in the speed profiles for the EPID and EBT3 results are consistent, but the EPID results are slightly better than the film results. Different speeds were measured for each MLC type. For all three MLC types, speed errors increase with increasing speed. The analysis speeds deviate from the preset speeds within approximately 0.01 cm s⁻¹. This quantitative analysis of MLC position and speed provides an intuitive evaluation for MLC quality assurance (QA).

The dosimetric impact of control point spacing for sliding gap MLC fields

Dynamic sliding gap multileaf collimator (MLC) fields are used to model MLC properties within the treatment planning system (TPS) for dynamic treatments. One of the key MLC properties in the Eclipse TPS is the dosimetric leaf gap (DLG) and precise determination of this parameter is paramount to ensuring accurate dose delivery. In this investigation, we report on how the spacing between control points (CPs) for sliding gap fields impacts the dose delivery, MLC positioning accuracy, and measurement of the DLG. The central axis dose was measured for sliding gap MLC fields with gap widths ranging from 2 to 40 mm. It was found that for deliveries containing two CPs, the central axis dose was underestimated by the TPS for all gap widths, with the maximum difference being 8% for a 2 mm gap field. For the same sliding gap fields containing 50 CPs, the measured dose was always within ±2% of the TPS dose. By directly measuring the MLC trajectories we show that this dose difference is due to a systematic MLC gap error for fields containing two CPs, and that the cause of this error is due to the leaf position offset table which is incorrectly applied when the spacing between CPs is too large. This MLC gap error resulted in an increase in the measured DLG of 0.5 mm for both 6 MV and 10 MV, when using fields with 2 CPs compared to 50 CPs. Furthermore, this change in DLG was shown to decrease the mean TPS‐calculated dose to the target volume by 2.6% for a clinical IMRT test plan. This work has shown that systematic MLC positioning errors occur for sliding gap MLC fields containing two CPs and that using these fields to model critical TPS parameters, such as the DLG, may result in clinically significant systematic dose calculation errors during subsequent dynamic MLC treatments.

PACS number(s): 87.56.nk

An EPID-based system for gantry-resolved MLC quality assurance for VMAT

Multileaf collimator (MLC) positions should be precisely and independently mea-sured as a function of gantry angle as part of a comprehensive quality assurance (QA) program for volumetric-modulated arc therapy (VMAT). It is also ideal that such a QA program has the ability to relate MLC positional accuracy to patient-specific dosimetry in order to determine the clinical significance of any detected MLC errors. In this work we propose a method to verify individual MLC trajectories during VMAT deliveries for use as a routine linear accelerator QA tool. We also extend this method to reconstruct the 3D patient dose in the treatment planning sys-tem based on the measured MLC trajectories and the original DICOM plan file. The method relies on extracting MLC positions from EPID images acquired at 8.41fps during clinical VMAT deliveries. A gantry angle is automatically tagged to each image in order to obtain the MLC trajectories as a function of gantry angle. This analysis was performed for six clinical VMAT plans acquired at monthly intervals for three months. The measured trajectories for each delivery were compared to the MLC positions from the DICOM plan file. The maximum mean error detected was 0.07 mm and a maximum root-mean-square error was 0.8 mm for any leaf of any delivery. The sensitivity of this system was characterized by introducing random and systematic MLC errors into the test plans. It was demonstrated that the system is capable of detecting random and systematic errors on the range of 1-2mm and single leaf calibration errors of 0.5 mm. The methodology developed in the work has potential to be used for efficient routine linear accelerator MLC QA and pretreatment patient-specific QA and has the ability to relate measured MLC positional errors to 3D dosimetric errors within a patient volume.

Determination of MLC model parameters for Monaco using commercial diode arrays

Multileaf collimators (MLCs) need to be characterized accurately in treatment planning systems to facilitate accurate intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT). The aim of this study was to examine the use of MapCHECK 2 and ArcCHECK diode arrays for optimizing MLC parameters in Monaco X‐ray voxel Monte Carlo (XVMC) dose calculation algorithm. A series of radiation test beams designed to evaluate MLC model parameters were delivered to MapCHECK 2, ArcCHECK, and EBT3 Gafchromic film for comparison. Initial comparison of the calculated and ArcCHECK‐measured dose distributions revealed it was unclear how to change the MLC parameters to gain agreement. This ambiguity arose due to an insufficient sampling of the test field dose distributions and unexpected discrepancies in the open parts of some test fields. Consequently, the XVMC MLC parameters were optimized based on MapCHECK 2 measurements. Gafchromic EBT3 film was used to verify the accuracy of MapCHECK 2 measured dose distributions. It was found that adjustment of the MLC parameters from their default values resulted in improved global gamma analysis pass rates for MapCHECK 2 measurements versus calculated dose. The lowest pass rate of any MLC‐modulated test beam improved from 68.5% to 93.5% with 3% and 2 mm gamma criteria. Given the close agreement of the optimized model to both MapCHECK 2 and film, the optimized model was used as a benchmark to highlight the relatively large discrepancies in some of the test field dose distributions found with ArcCHECK. Comparison between the optimized model‐calculated dose and ArcCHECK‐measured dose resulted in global gamma pass rates which ranged from 70.0%–97.9% for gamma criteria of 3% and 2 mm. The simple square fields yielded high pass rates. The lower gamma pass rates were attributed to the ArcCHECK overestimating the dose in‐field for the rectangular test fields whose long axis was parallel to the long axis of the ArcCHECK. Considering ArcCHECK measurement issues and the lower gamma pass rates for the MLC‐modulated test beams, it was concluded that MapCHECK 2 was a more suitable detector than ArcCHECK for the optimization process.

PACS number(s): 87.55.Qr