Dosimetric characterization of a new surface-conforming electron MLC prototype.

The purpose is to reduce normal tissue radiation toxicity for electron therapy through the creation of a surface-conforming electron multileaf collimator (SCEM). The SCEM combines the benefits of skin collimation, electron conformal radiotherapy, and modulated electron radiotherapy. An early concept for the SCEM was constructed. It consists of leaves that protrude towards the patient, allowing the leaves to conform closely to irregular patient surfaces. The leaves are made of acrylic to decrease bremsstrahlung, thereby decreasing the out-of-field dose. Water tank scans were performed with the SCEM in place for various field sizes for all available electron energies (6, 9, 12, and 15 MeV) with a 0.5 cm air gap to the water surface at 100 cm source-to-surface distance (SSD). These measurements were compared with Cerrobend cutouts with the field size-matched at 100 and 110 cm SSD. Output factor measurements were taken in solid water for each energy at dmax for both the cerrobend cutouts and SCEM at 100 cm SSD. Percent depth dose (PDD) curves for the SCEM shifted shallower for all energies and field sizes. The SCEM also produced a higher surface dose relative to Cerrobend cutouts, with the maximum being a 9.8% increase for the 3 cm × 9 cm field at 9 MeV. When compared to the Cerrobend cutouts at 110 cm SSD, the SCEM showed a significant decrease in the penumbra, particularly for lower energies (i.e., 6 and 9 MeV). The SCEM also showed reduced out-of-field dose and lower bremsstrahlung production than the Cerrobend cutouts. The SCEM provides significant improvement in the penumbra and out-of-field dose by allowing collimation close to the skin surface compared to Cerrobend cutouts. However, the added scatter from the SCEM increases shallow PDD values. Future work will focus on reducing this scatter while maintaining the penumbra and out-of-field benefits the SCEM has over conventional collimation.

Monte Carlo modeling of the Elekta Versa HD and patient dose calculation with EGSnrc/BEAMnrc.

INTRODUCTION: Numerous studies have proven the Monte Carlo method to be an accurate means of dose calculation. Although there are several commercial Monte Carlo treatment planning systems (TPSs), some clinics may not have access to these resources. We present a method for routine, independent patient dose calculations from treatment plans generated in a commercial TPS with our own Monte Carlo model using free, open-source software. MATERIALS AND METHODS: A model of the Elekta Versa HD linear accelerator was developed using the EGSnrc codes. A MATLAB script was created to take clinical patient plans and convert the DICOM RTP files into a format usable by EGSnrc. Ten patients' treatment plans were exported from the Monaco TPS to be recalculated using EGSnrc. Treatment simulations were done in BEAMnrc, and doses were calculated using Source 21 in DOSXYZnrc. Results were compared to patient plans calculated in the Monaco TPS and evaluated in Verisoft with a gamma criterion of 3%/2 mm. RESULTS: Our Monte Carlo model was validated within 1%/1-mm accuracy of measured percent depth doses and profiles. Gamma passing rates ranged from 82.1% to 99.8%, with 7 out of 10 plans having a gamma pass rate over 95%. Lung and prostate patients showed the best agreement with doses calculated in Monaco. All statistical uncertainties in DOSXYZnrc were less than 3.0%. CONCLUSION: A Monte Carlo model for routine patient dose calculation was successfully developed and tested. This model allows users to directly recalculate DICOM RP files containing patients' plans that have been exported from a commercial TPS.

Incorporating biological modeling into patient-specific plan verification.

PURPOSE: Dose-volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. METHODS: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient-specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. RESULTS: The complication-free tumor control probability, P+ , has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (-3.9% ± 5.8%), head-neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). CONCLUSION: Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.

Standardizing and improving dose predictions for head and neck cancers using complete sets of OAR contours.

BACKGROUND: Radiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning-based dose prediction models have not been studied. PURPOSE: The purpose of this study was to investigate the impacts of incomplete contour sets in the context of deep learning-based radiotherapy dose prediction models trained with clinical datasets and to introduce a novel data substitution method that utilizes automated contours for undefined structures. METHODS: We trained Standard U-Nets and Cascade U-Nets to predict the volumetric dose distributions of patients with head and neck cancers (HNC) using three input variations to evaluate the effects of missing contours, as well as a novel data substitution method. Each architecture was trained with the original contour (OC) inputs, which included missing information, hybrid contour (HC) inputs, where automated OAR contours generated in software were substituted for missing contour data, and automated contour (AC) inputs containing only automated OAR contours. 120 HNC treatments were used for model training, 30 were used for validation and tuning, and 44 were used for evaluation and testing. Model performance and accuracy were evaluated with global whole body dose agreement, PTV coverage accuracy, and OAR dose agreement. The differences in these values between dataset variations were used to determine the effects of missing data and automated contour substitutions. RESULTS: Automated contours used as substitutions for missing data were found to improve dose prediction accuracy in the Standard U-Net and Cascade U-Net, with a statistically significant difference in some global metrics and/or OAR metrics. For both models, PTV coverage between input variations was unaffected by the substitution technique. Automated contours in HC and AC datasets improved mean dose accuracy for some OAR contours, including the mandible and brainstem, with a greater improvement seen with HC datasets. Global dose metrics, including mean absolute error, mean error, and percent error were different for the Standard U-Net but not for the Cascade U-Net. CONCLUSION: Automated contours used as a substitution for contour data improved prediction accuracy for some but not all dose prediction metrics. Compared to the Standard U-Net models, the Cascade U-Net achieved greater precision.

Evaluation of the Elekta Agility MLC performance using high-resolution log files.

PURPOSE: With the advent of volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) treatment techniques, the requirement for more elaborate approaches in reviewing linac components' integrity has become even more stringent. A possible solution to this challenge is to employ the usage of log files generated during treatment. The log files generated by the new generation of Elekta linacs record events at a higher frequency (25 Hz) than their predecessors, which allows for retrospective analysis and identification of subtle changes and provides another means of quality assurance. The ability to track machine components based on log files for each treatment can allow for constant monitoring of fraction consistency in addition to machine reliability. Using Elekta Agility log files, a set of tests were developed to evaluate the reliability and robustness of the multileaf collimators (MLCs). METHODS: To evaluate Elekta log file utilization for linac MLC QA effectiveness, five MLC test patterns were constructed to review the effects of leaf velocity and acceleration on positional accuracy, including gravitational effects for the Elekta MLC system. Each test was run five times in a particular setting to obtain reproducibility data and statistical averages. This study was performed on two identical Versa HD machines, each delivering a full set of test plans with all possible variations. Plans were delivered using Elekta's iCOMcat software and recorded log files were extracted. Log files were reformatted for readability and automatically analyzed in Matlab® . RESULTS: The Elekta Agility MLC system was shown to be capable of obtaining speeds within the range of 5-35 mm/s. MLC step and shoot tests have demonstrated the MLC system's capability of having positional repeatability, averaging 0.03- and 0.08-mm offsets with and without gravitational effects, respectively. The IMRT-specific tests have shown that gravitational effects are negligible with all positional tests averaging 0.5-mm offsets. The largest speed root-mean-square error (RMSE) for the MLC system was found at the maximum speed of 35 mm/s with an average error of 0.8 mm. For slower speeds, the value was found to be much lower. CONCLUSION: Utilizing log files has demonstrated the feasibility for higher precision of MLC motions to be reviewed, based on the performance tests that were instituted. Log files provide insight on the effects of friction, acceleration, and gravity, with MU's delivered that previously could not be reviewed in such detail. Based on our results, log file-based QA has enhanced our ability to review performance, functionality, and perform QA on Elekta's MLC system.