Patient-specific Intensity Modulated Radiation Therapy Research Articles

EVALUATION OF PATIENT-SPECIFIC IMRT QUALITY ASSURANCE AND POINT DOSE MEASUREMENT FOR COMPLEX HEAD AND NECK AND BRAIN CANCER USING GAFCHROMIC EBT3 FILM.

Patient-specific intensity-modulated radiation therapy (IMRT) quality assurance (QA) is essential for complex radiotherapy treatment as it involves complex intensity modulation and high-dose gradient regions. IMRT QA was performed by point dose verification and two-dimensional (2D) dose distribution measurement using gamma method. Calibrated External Beam Therapy 3 (EBT3) film was used for point dose and pre-treatment verification of 10 IMRT plans, five complex Head and Neck (HN) and five brain cases. The gamma passing rate (GPR) was evaluated for 3%/3mm gamma criteria and compared with 2D array. Isocentre dose was measured for all 10 IMRT plans on EBT3 film. Percentage deviation of point dose measurement from TPS calculated was found 0.4% for brain cases and 2.9% for HN cases. The GPR for 3%/3mm criteria was obtained higher than 95% for brain and HN cases. Results suggest that film dosimetry is also a reliable verification system for patient-specific IMRT QA as the 2D array.

Direct incorporation of patient-specific efficacy and toxicity estimates in radiation therapy plan optimization.

Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients. We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity. The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

Gamma Passing Rate – Correlation with Patient Specific Quality Assurance Devices using Two Photon energies in Pelvic IMRT

The effect on Gamma Passing Rate is studied by changing the Photon Energy in the Patient Specific Quality Assurancein Pelvic IMRT using EPID and MatriXX keeping PSQA device at Isocenter. Dose verification of 62 patients, sufferingfrom pelvic malignancies and treated with Intensity modulated radiation therapy (IMRT), with EPID and MatriXX usinggamma criteria of 3%/3 mm, 3%/2 mm, 2%/3 mm, 2%/2 mm and investigated the effect on Gamma Passing Rate (%GP) bychanging beam energy from 6 MV to 10 MV. The results demonstrated that the behavior of gamma passing rate decreases ascriteria is changed from 3%/3 mm to strict criteria. The mean gamma passing rate for 6 MV beam were (%GP±StandardDeviation σ) 99.64±0.81, 99.45±1.08, 97.57±2.12, 96.17±2.63 using the criteria 3%/3 mm, 3%/2 mm, 2%/3 mm, 2%/2 mmrespectively in EPID. For MatriXX, the mean %GP were 96.69±4.11, 93.85±5.31, 88.17±10.63, 82.2±12.28 respectively.For 10 MV, the mean (%GP±Std. Dev) were 99.64±0.81, 99.45±1.08, 97.57±2.12, 96.17±2.63 for in EPID, 96.69±4.11,93.85±5.31, 88.17±10.63, 82.2±12.28 for MatriXX respectively. Difference between %GP for 6 MV and 10 MV beam were-0.01, -0.11, 1.08, 1.61 with gamma criteria of 3%/3 mm, 3%/2 mm, 2%/3 mm, 2%/2 in EPID. For MatriXX, differencebetween %GP for 6 MV and 10 MV beam were -0.40, -0.24, 0.96, 1.62 respectively. It can be concluded that applying morestrict gamma criteria results in low gamma passing rate. There is marked difference in Gamma Passing Rate (%GP) withchange in photon energy.

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy

This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans. Ninety cervical cancer clinical IMRT plans were collected to train a two-stage convolution neural network, of which 66 plans were assigned for training, 11 for validation, and 13 for test. The neural network took patients' computed tomography (CT) anatomy as the input and predicted the fluence map for each radiation beam. The predicted fluence maps were then imported into a treatment planning system and converted to multileaf collimators motion sequences. The automatic plan was evaluated against its corresponding clinical plan, and its machine deliverability was validated by patient-specific IMRT quality assurance (QA). There were no significant differences in dose parameters between automatic and clinical plans for all 13 test patients, indicating a good prediction of fluence maps and a decent quality of automatic plans. The average dice similarity coefficient of isodose volumes encompassed by 0%-100% isodose lines ranged from 0.94 to 1. In patient-specific IMRT QA, the mean gamma passing rate of automatic plans achieved 99.5% under 3%/3mm criteria, and 97.3% under 2%/2mm criteria, with a low dose threshold of 10%. The proposed deep learning framework can produce machine-deliverable IMRT plans with quality similar to the clinical plans in the test set. It skips the inverse plan optimization process and provides an effective and efficient method to accelerate treatment planning process.

Survey of IMRT in Japan

This study evaluated the current status, quality assurance (QA) and technique of intensity-modulated radiotherapy (IMRT) in Japan.IMRT is a new technique, introduced in the late 1990s. Since 2000, the number of institutions providing IMRT has increased. Therefore, to recognize the current status of IMRT in Japan, a nation-wide survey was conducted by the Japan 3-Dimensional Conformal External Beam Radiotherapy Group (J-CERG). The questionnaires, which consists of the clinical part and physics part, was sent by mail or e-mail to 835 institutions. By the end of Feb. 2021, 427 institutions and 534 institutions responded to the clinical part and physics part, respectively.In the clinical part, Two-hundreds and ninety-two (68%) institutions indicated that they have used IMRT technique for 44,409 cases out of 177,285 total in a year of 2019. Among these patients, 10,905 (25%) were prostate cancer, 7,772(18%) were head & neck cancer, 2,621 (6%) brain tumor, 2,027 (5%) lung tumor for SBRT, followed by liver tumor for SBRT, GYN cancer, esophageal cancer, breast cancer and GI tumor. The percentage of IMRT in all treatment was 75-100% in 18 institutions (6%), 50-75% in 22 institutions (8%), 20-50% in 144 institutions (49%), 10-20% in 68 institutions (23%), 5-10% in 28 institutions (10%), 5-1% in 11 institutions (4%) and < 1% in 2 institutions (1%). Target and OAR delineation was done by physician in 92% institutions and 69% institutions, respectively. In the physics part, 340 institutions (64%) use IMRT technique, and 80 institutions (24%) have commissioned their treatment planning systems with the beam data provided by the manufacturer. The measurement-based patient specific IMRT QAs are performed by 99% of institutions. The dose distribution measurement on films, 3D arrays, and 2D arrays are 42%, 41%, and 16%, respectively.The updated status of IMRT in 2019 in Japan was surveyed. The number of institutions where IMRT was started continuously increased in number.

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric‐arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method for QA. This approach allows the verification of the accuracy of the dose calculation, data transfer, and the delivery system. However, patient‐specific QA procedures are expensive and require significant time and effort by the physicists. Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated. Various ML and DL models have shown promising prediction accuracy and a high potential as time‐efficient virtual QA tool. In this paper, we review the ML and DL based models that were developed for patient specific IMRT and VMAT QA outcome predictions from algorithmic and clinical applicability perspectives. We focus on comparing the algorithms, the dataset sizes, the input parameters and features, the QA outcome prediction approaches, the validation, the performance, the clinical applicability, and the potential clinical impact. In addition, we discuss the present challenges as well as the future directions in the implementation of these models. To the best of our knowledge, this is the first review on the application of ML and DL based models in IMRT/VMAT QA predictions.

Comprehensive Patient-Specific Intensity-Modulated Radiation Therapy Quality Assurance Comparing Mobius3D/FX to Conventional Methods of Evaluation.

PurposeTo determine the appropriateness of implementing Mobius3D/FX (Varian Medical Systems, Inc., Palo Alto, CA, USA) as not only a pretreatment secondary check but as an alternative to measurement-based patient-specific intensity-modulated radiation therapy (IMRT) quality assurance (QA).MethodsMobius3D/FX was commissioned and stock beam models were tweaked so that an independent recalculated 3D dose distribution can be obtained. Then, 50 patient-specific treatment plans for various indications were delivered across a 2D ion chamber array, radiochromic film setup, and electronic portal imager and analyzed with MobiusFX and gamma analysis. The concordance of plans scored as passing between MobiusFX and the conventional methods of QA was determined.ResultsAll analyzed treatment plans passed with a gamma passing rate >90% across all conventional QA methods, most commonly using a 3%/3mm gamma criterion except for film measurements where a 5%/3mm criterion was applied. There was good agreement and concordance between MobiusFX and conventional methods when using a 3%/3mm criteria for MobiusFX, whereas a 2%/2mm criteria appeared too stringent as it failed treatment plans deemed clinically acceptable using conventional methods.ConclusionsUsing a 50-sample subset of clinically delivered treatment plans this non-inferiority-type comparison shows Mobius3D/FX based on log file analysis to be a suitable alternative to conventional QA methods when utilizing the 3%/3mm gamma criterion. Methods based on log file analysis can provide an opportunity for resource sparing, improving the efficiency, and workflow for evaluating IMRT treatment plans.

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

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. 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. 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 3mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. 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.

Patient-specific radiotherapy quality assurance for estimating actual treatment dose

This study aimed to evaluate the optimal method for planning computed tomography (CT) for prostate cancer radiotherapy to avoid a dose difference of ≥3% between the actual and planned treatments using multiple acquisition planning CT (MPCT). We calculated the 3-dimensional (3D) displacement error between the pelvic bone and matching fiducial marker on MPCT and cone-beam CT scans of 25 patients who underwent prostate volumetric-modulated arc therapy for prostate cancer. The correlation of the 3D displacement error and the dose difference between planned and actual treatments was calculated using least squares second-order polynomial model. The 3D displacement error showed a moderate correlation with differences between planned and accumulated treatment doses (r = 0.587, p < 0.0001). Moreover, the improvement rate of the minimum 3D displacement error showed a strong correlation with the relative error between each MPCT image (r = 0.793, p < 0.0001). Significant differences were observed between planned and actual treatment doses (p < 0.0001) in the relative 3D displacement errors of <1 mm, 1 to 3 mm, and >3 mm. The 3D displacement error on MPCT (as the selection estimation index for optimal planning CT) is useful for monitoring patient-specific intensity-modulated radiation therapy quality assurance. This new method allows to estimate dose differences from the planned dose before commencing treatment, thereby ensuring high-quality therapy. As radiotherapy quality is critical for patient outcome, these findings may contribute to better management of prostate cancer.

Error detection and classification in patient-specific IMRT QA with dual neural networks.

Despite being the standard metric in patient-specific quality assurance (QA) for intensity-modulated radiotherapy (IMRT), gamma analysis has two shortcomings: (a) it lacks sensitivity to small but clinically relevant errors (b) it does not provide efficient means to classify the error sources. The purpose of this work is to propose a dual neural network method to achieve simultaneous error detection and classification in patient-specific IMRT QA. For a pair of dose distributions, we extracted the dose difference histogram (DDH) for the low dose gradient region and two signed distance-to-agreement (sDTA) maps (one in x direction and one in y direction) for the high dose gradient region. An artificial neural network (ANN) and a convolutional neural network (CNN) were designed to analyze the DDH and the two sDTA maps, respectively. The ANN was trained to detect and classify six classes of dosimetric errors: incorrect multileaf collimator (MLC) transmission (±1%) and four types of monitor unit (MU) scaling errors (±1% and ±2%). The CNN was trained to detect and classify seven classes of spatial errors: incorrect effective source size, 1mm MLC leaf bank overtravel or undertravel, 2mm single MLC leaf overtravel or undertravel, and device misalignment errors (1mm in x- or y direction). An in-house planar dose calculation software was used to simulate measurements with errors and noise introduced. Both networks were trained and validated with 13 IMRT plans (totaling 88 fields). A fivefold cross-validation technique was used to evaluate their accuracy. Distinct features were found in the DDH and the sDTA maps. The ANN perfectly identified all four types of MU scaling errors and the specific accuracies for the classes of no error, MLC transmission increase, MLC transmission decrease were 98.9%, 96.6%, and 94.3%, respectively. For the CNN, the largest confusion occurred between the 1-mm-MLC bank overtravel class and the 1-mm-device alignment error in x-direction class, which brought the specific accuracies down to 90.9% and 92.0%, respectively. The specific accuracy for the 2-mm-single MLC leaf undertravel class was 93.2% as it misclassified 5.7% of the class as being error free (false negative). Otherwise, the specific accuracy was above 95%. The overall accuracies across the fivefold were 98.3±0.7% and 95.6%±1.5% for the ANN and the CNN, respectively. Both the DDH and the sDTA maps are suitable features for error classification in IMRT QA. The proposed dual neural network method achieved simultaneous error detection and classification with excellent accuracy. It could be used in complement with the gamma analysis to potentially shift the IMRT QA paradigm from passive pass/fail analysis to active error detection and root cause identification.

Dosimetric evaluation of phantoms including metal objects with high atomic number for use in intensity modulated radiation therapy.

The dosimetric effect of artefacts caused by metal hip prostheses in computed tomography imaging is most commonly encountered in the planning of prostate cancer treatment. In this study, a phantom, containing a metal with high atomic number, was prepared for intensity-modulated radiotherapy (IMRT) treatment plans to be used in quality assurance (QA) procedures. Two sets of image files, one without metal artefact correction (ORG) and another with MAR correction (MAR+), were sent to the treatment planning system. In this study, 12 IMRT treatment plans with different fields and segment numbers were calculated. The normal tissue complication probability (NTCP) values of imaginary organs at risk (OARs), such as the rectum and bladder, were investigated, as was the difference in dose maps for ORG and MAR+ derived by calculating gamma passing rates (GPRs). The MatriXX was used for the gamma evaluation of patient-specific IMRT QA measurements. The gamma evaluation was repeated, based on the measurements using an EBT3 gafchromic film, for the plan showing the lowest GPR. The mean relative difference in NTCP values between the two sets of image files was found to be 2.5, 2.1 and 1.4 for the rectum; and 5.33, 6.80 and 9.82 for the bladder, for the investigated 5-, 7- and 9-field beam arrangements, respectively. The relative differences and the standard deviations in GPRs for the standard and metal-containing phantoms were calculated for the MAR+ and ORG sets. The maximum difference found was 7.69% ± 0.88 for the 9-field beam arrangement calculated without metal artefact correction. In the IMRT QA procedures for prostate patients with hip prostheses, the application of a metal-containing phantom that is both easy and inexpensive to prepare, is considered to be a useful method for examining any dose changes involved in introducing a hip prosthesis. Therefore, it is recommended for use in clinics that do not have MAR correction algorithms.

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

PurposeTreatment 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 MaterialsAn 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.ResultsDose 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.ConclusionsSmall 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.

Feasibility Study of the Fluence-to-Dose Network (FDNet) for Patient-Specific IMRT Quality Assurance

The aim of this study is to predict the delivered dose distribution [Ddelivered(x, y)] with the use of a fluence-to-dose network (FDNet) to conduct patient-specific intensity-modulated radiation therapy (IMRT) quality assurance (pQA). The architecture of the FDNet was based on a convolutional neural network. Forty-four IMRT clinical cases of planned dose distributions for pQA [Dplanned(x, y)] and dynamic multileaf collimator (MLC) log files (Dynalog files) were collected. Using the Dynalog files, the expected fluence stack [Fexpected(x, y, t)] and the actual fluence stack [Factual(x, y, t)] were created from the expected and the actual machine parameters, respectively. The actual fluence stack, which was reconstructed from the partial information of the Dynalog file, corresponded to the control points of the Digital Imaging and Communications in Medicine radiation treatment plan and was denoted as [Factual(x, y, tpartial)]. The entire dataset was split into 11 subsets for the k-fold averaging cross-validation (k = 11). Ten (out of the 11) folds were used to train 10 candidate optimal FDNet models, and an ultimate FDNet was determined by averaging the parameters of the optimal models. The pQA was performed using the test data of the remaining fold with the ultimate FDNet. The dose distributions predicted using Factual(x, y, t) [Dpredicted(Factual(x, y, t))] and Factual(x, y, tpartial) [Dpredicted(Factual(x, y, tpartial))] were acquired. To evaluate the predicted pQA results, we conducted dosimetry using EBT3 films and an ion-chamber array detector (MatriXX). These dose distributions were compared with the Dplanned(x, y) by using a gamma analysis. The average gamma passing rates were determined based on the 3%/3 mm gamma criterion and were, respectively, equal to 98.49%, 97.21%, 97.23%, and 98.03%, for the Dpredicted(Factual(x, y, t)), Dpredicted(Factual(x, y, tpartial)), EBT3 film, and MatriXX. According to this study, the feasibility of the dose prediction method using the FDNet with complete Dynalog information was verified for the pQA. The respective differences of the average gamma passing rates for the Dpredicted(Factual(x, y, t)), and Dpredicted(Factual(x, y, tpartial)) were equal, respectively, to 1.28% and 2.88% according to the 3%/3 mm and the 2%/2 mm gamma criteria.

Comparison of patient-specific intensity modulated radiation therapy quality assurance for the prostate across multiple institutions.

To evaluate the success of a patient-specific intensity modulated radiation therapy (IMRT) quality assurance (QA) practice for prostate cancer patients across multiple institutions using a questionnaire survey. The IMRT QA practice involves different methods of dose distribution verification and analysis at different institutions. Two full-arc volumetric modulated arc therapy (VMAT) plan and 7 fixed-gantry IMRT plan with DMLC were used for patient specific QA across 22 institutions. The same computed tomography image and structure set were used for all plans. Each institution recalculated the dose distribution with fixed monitor units and without any modification. Single-point dose measurement with a cylindrical ionization chamber and dose distribution verification with a multi-detector or radiochromic film were performed, according to the QA process at each institution. Twenty-two institutions performed the patient-specific IMRT QA verifications. With a single-point dose measurement at the isocenter, the average difference between the calculated and measured doses was 0.5 ± 1.9%. For the comparison of dose distributions, 18 institutions used a two or three-dimensional array detector, while the others used Gafchromic film. In the γ test with dose difference/distance-to-agreement criteria of 3%-3 mm and 2%-2 mm with a 30% dose threshold, the median gamma pass rates were 99.3% (range: 41.7%-100.0%) and 96.4% (range: 29.4%-100.0%), respectively. This survey was an informative trial to understand the verification status of patient-specific IMRT QA measurements for prostate cancer. In most institutions, the point dose measurement and dose distribution differences met the desired criteria.

Independent recalculation outperforms traditional measurement-based IMRT QA methods in detecting unacceptable plans.

To evaluate the performance of an independent recalculation and compare it against current measurement-based patient specific intensity-modulated radiation therapy (IMRT) quality assurance (QA) in predicting unacceptable phantom results as measured by the Imaging and Radiation Oncology Core (IROC). When institutions irradiate the IROC head and neck IMRT phantom, they are also asked to submit their internal IMRT QA results. Separately from this, IROC has previously created reference beam models on the Mobius3D platform to independently recalculate phantom results based on the institution's DICOM plan data. The ability of the institutions' IMRT QA to predict the IROC phantom result was compared against the independent recalculation for 339 phantom results collected since 2012. This was done to determine the ability of these systems to detect failing phantom results (i.e., large errors) as well as poor phantom results (i.e., modest errors). Sensitivity and specificity were evaluated using common clinical thresholds, and receiver operator characteristic (ROC) curves were used to compare across different thresholds. Overall, based on common clinical criteria, the independent recalculation was 12 times more sensitive at detecting unacceptable (failing) IROC phantom results than clinical measurement-based IMRT QA. The recalculation was superior, in head-to-head comparison, to the EPID, ArcCheck, and MapCheck devices. The superiority of the recalculation vs these array-based measurements persisted under ROC analysis as the recalculation curve had a greater area under it and was always above that for these measurement devices. For detecting modest errors (poor phantom results rather than failing phantom results), neither the recalculation nor measurement-based IMRT QA performed well. A simple recalculation outperformed current measurement-based IMRT QA methods at detecting unacceptable plans. These findings highlight the value of an independent recalculation, and raise further questions about the current standard of measurement-based IMRT QA.

The sensitivity of patient-specific IMRT QA methods in detecting systematic errors: field-by-field versus single-gantry-angle composite

This study evaluated the effect of small systematic errors, such as those from a multileaf collimator (MLC), on the quality of intensity modulated radiotherapy (IMRT) treatment plan delivery. Two IMRT quality assurance (QA) verification techniques, field-by-field (FBF) and singe-gantry-angle composite (SGAC), were performed to evaluate both original and modified plans using a 2D ion chamber array detector. The dose distributions measured by the array detector for both FBF and SGAC were compared with the dose distribution calculated by the treatment planning system (TPS). FBF was found to be more sensitive than SGAC at detecting small systematic errors such as the opening and closing of the MLC’s segments, which were evaluated with respect to a gamma-index of 3%/3 mm and 2%/2 mm. The systematic errors involved in closing the segments of the anterior field by 2 mm and 3 mm showed a significant difference compared with the original field (unmodified): 83.1 ± 1.7% and 42.9 ± 1.9% gamma-index passing rates, respectively, for FBF. For SGAC, the magnitude of closing the MLC by 2 mm remained unnoticed and resulted in a 95.1 ± 2.61% gamma-index passing rate. Opening the MLC by 2 mm gave a false negative, but more than 5% of the rectum received 75 Gy, which exceeded the tolerance radiation dose recommended by the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC).

222. Optimization of pretreatment IMRT dose distribution verification: Bidimensional gaussian kernel implementation

Purpose Intensity modulated radiation therapy (IMRT) is a widely used technique delivering a highly conformal dose to the target together with a better sparing of organs at risk with respect to conventional irradiation techniques. Quality assurance (QA) of the generated IMRT treatment plan is of most importance to ensure that the patient receives the planned dose; a two-dimensional array of ionization chambers in a solid phantom can be used for the verification. Different methods exist for comparison of dose distributions and the gamma evaluation is the most commonly used. The aim of this work is to convolve the calculated dose distributions in order to optimize the comparison with the measured data obtained with the MatriXX ionization chamber detector arrays (IBA Dosimetry, Germany). Methods In this study a bidimensional gaussian kernel, which addresses the ionization chamber volume averaging effect, has been implemented to optimize the comparison of dose distributions for pretreatment patient-specific IMRT QA. It was determined by scanning a single chamber with a slit beam. A point spread function was reconstructed with a Gaussian function obtained with a MATLAB script. The kernel has been tested on head&neck and prostate treatment plans. Results It has been found that the convolution of the kernel with the treatment planning system calculated dose distribution can improve the comparison respect to the conventional method of IMRT verification. The gamma index passing rate values found with convolution method are higher in all considered clinical cases. Conclusions Intensity modulated radiation therapy (IMRT) is a widely used technique delivering a highly conformal dose to the target together with a better sparing of organs at risk with respect to conventional irradiation techniques. Quality assurance (QA) of the generated IMRT treatment plan is of most importance to ensure that the patient receives the planned dose; a two-dimensional array of ionization chambers in a solid phantom can be used for the verification. Different methods exist for comparison of dose distributions and the gamma evaluation is the most commonly used. The aim of this work is to convolve the calculated dose distributions in order to optimize the comparison with the measured data obtained with the MatriXX ionization chamber detector arrays (IBA Dosimetry, Germany). In this study a bidimensional gaussian kernel, which addresses the ionization chamber volume averaging effect, has been implemented to optimize the comparison of dose distributions for pretreatment patient-specific IMRT QA. It was determined by scanning a single chamber with a slit beam. A point spread function was reconstructed with a Gaussian function obtained with a MATLAB script. The kernel has been tested on head&neck and prostate treatment plans. It has been found that the convolution of the kernel with the treatment planning system calculated dose distribution can improve the comparison respect to the conventional method of IMRT verification. The gamma index passing rate values found with convolution method are higher in all considered clinical cases.

Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions

To improve the detection of errors in intensity-modulated radiation therapy (IMRT) with a novel method that uses quantitative image features from radiomics to analyze gamma distributions generated during patient specific quality assurance (QA). One hundred eighty-six IMRT beams from 23 patient treatments were delivered to a phantom and measured with electronic portal imaging device dosimetry. The treatments spanned a range of anatomic sites; half were head and neck treatments, and the other half were drawn from treatments for lung and rectal cancers, sarcoma, and glioblastoma. Planar gamma distributions, or gamma images, were calculated for each beam using the measured dose and calculated doses from the 3-dimensional treatment planning system under various scenarios: a plan without errors and plans with either simulated random or systematic multileaf collimator mispositioning errors. The gamma images were randomly divided into 2 sets: a training set for model development and testing set for validation. Radiomic features were calculated for each gamma image. Error detection models were developed by training logistic regression models on these radiomic features. The models were applied to the testing set to quantify their predictive utility, determined by calculating the area under the curve (AUC) of the receiver operator characteristic curve, and were compared with traditional threshold-based gamma analysis. The AUC of the random multileaf collimator mispositioning model on the testing set was 0.761 compared with 0.512 for threshold-based gamma analysis. The AUC for the systematic mispositioning model was 0.717 versus 0.660 for threshold-based gamma analysis. Furthermore, the models could discriminate between the 2 types of errors simulated here, exhibiting AUCs of approximately 0.5 (equivalent to random guessing) when applied to the error they were not designed to detect. The feasibility of error detection in patient-specific IMRT QA using radiomic analysis of QA images has been demonstrated. This methodology represents a substantial step forward for IMRT QA with improved sensitivity and specificity over current QA methods and the potential to distinguish between different types of errors.

Correcting TG 119 confidence limits.

Task Group 119 (TG-119) has been adopted for evaluating the adequacy of intensity-modulated radiation therapy (IMRT) commissioning and for establishing patient-specific IMRT quality assurance (QA) passing criteria in clinical practice. TG-119 establishes 95% confidence limits (CLs), which help clinics identify systematic IMRT QA errors and identify outliers. In TG-119, the 95% CLs are established by fitting the Gamma Γ analysis passing rate results to an assumed distribution, then calculating the limit in which 95% of the data fall. CLs for a given dataset will depend greatly on the type of distribution used, and those determined by following the TG-119 guidelines are only valid if the underlying data follows a Gaussian distribution. Gaussian distributions assume symmetry about the mean, which would imply the possibility of negative Γ analysis failing rates. This study demonstrates that the gamma distribution is a more reasonable assumption for establishing CLs than the Gaussian distribution used in TG-119. Thus, the gamma distribution is suggested as a replacement to the conventional Gaussian statistical model used in TG-119. The moments estimator (ME) for the gamma family is used to obtain the CLs of the failing rates for all Γ analysis criteria. To demonstrate the congruence of the gamma distribution, the root mean squared error and the CL values for the MEs of the gamma and the Gaussian families were compared. In this study, the empirical 95% CLs generated using 302 plans represent the ground truth, which resulted in a 91.83% passing rate using 3%/3 mm error local criteria. The gamma distribution underestimates the 95% CL by 0.09%, while the Gaussian distribution overestimates the 95% CL by 4.12%. Although IMRT QA equipment may vary between clinics, the mathematical formalism presented in this study applies to any combination of planning and delivery systems. This study has demonstrated that a gamma distribution should be favored over a Gaussian distribution when establishing CLs for IMRT QA.

Comparison of Gamma Pass Rate between the Dose-to-Water and Dose-to-Medium Reporting Modes for Patient-Specific QA Using a Helical Diode Array Dosimeter with a Fixed Phantom Density

Introduction: To compare the measured dose distributions to calculated ones in dose-to-water (Dw) and dose-to-medium (Dm) reporting modes for simple plans and patient-specific intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) plans using ArcCHECK with a fixed phantom density. Methods: The recommended density value of 1.18 g/cm3 for Acuros XB and X-ray voxel Monte Carlo was assigned to ArcCHECK on CT images. A total of 45 simple plans, including a 1-field plan, a 3-field plan, a 4-field plan, a half-arc plan from 270° to 90°, and a full-arc plan, were assessed. Subsequently, the patient-specific 96 IMRT and VMAT plans were evaluated. Gamma analysis with a 3% normalized global dose error and a 3 mm distance-to-agreement criteria (γ3%G/3mm) was performed in the Dw and Dm. The change in γ3%G/3mm between Dw and Dm were statistically analyzed using JMPPro11 software. Results: The median values of γ3%G/3mm for all simple plans for Dw and Dm were 98.1% (range, 75.2% - 100%) and 95.5% (range, 23.7% - 100%), respectively (p < 0.01). In the patient-specific IMRT and VMAT plans, the median values of γ3%G/3mm for Dw and Dm were 98.6% (range, 90.1% - 100%) and 90.5% (range, 38.5% - 97.2%), respectively (p < 0.01). Conclusion: Our results showed that the calculated and measured dose distributions were in good agreement for Dw, but were not for Dm. From the viewpoint of the rationale of dosimetry, Dw shows better agreement with measured dose distribution when using the fixedphantom density recommended by the vendor.