Inter-unit variability of multi-leaf collimator parameters for IMRT and VMAT treatment planning: a multi-institutional survey

A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

PURPOSE

To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy.

METHODS

Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric.

RESULTS

Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites.

CONCLUSION

Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Assessing quality assurance of multi-leaf collimator using the structural similarity index

PURPOSE

This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic.

METHOD

To assess sensitivity to MLC positioning errors, we designed a 1 cm slit on the reference MLC, subsequently shifted by 0.5-5 mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25 cm × 25 cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25 mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored.

RESULTS

The Gamma index test started detecting the MLC shift at a threshold of 4 mm, whereas the SSI* metric showed sensitivity to shifts as small as 2 mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1 mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam.

CONCLUSIONS

Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy

PURPOSE

The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG).

METHODS

A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters ofσ = 0.5 $\sigma = 0.5$ and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison.

RESULTS

The detection and classification of TF and DLG error were feasible forσ = 0.5 $\sigma = 0.5$ ; however, a considerable reduction of accuracy was observed forσ = 1.0 $\sigma = 1.0$ depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models forσ = 0.5 $\sigma = 0.5$ and 1.0. The gamma analysis could not detect errors.

CONCLUSIONS

We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

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.

[Utility of Point Dose Verification Using Measured Ionization Amount Ratio of Reference Irradiation to Volumetric Modulated Arc Therapy]

In the point dose verification of intensity-modulated radiation therapy (IMRT), we compared the commonly used method of measuring absolute dose (absolute method) with the measurement method by the American Association of Physicists in Medicine Task Group 119, which describes the point dose verification for IMRT using the ratio of reference irradiation and measured ionization. The target was 66 plans for head and neck cancer, 46 plans for lung cancer with 6 MV X-ray, and 31 plans for prostate cancer with 10 MV X-ray. They were treated with volumetric-modulated arc therapy (VMAT). Each plan was evaluated by the absolute method and the TG119 method using 3D-array. The average and 2SD of the verification results for head and neck cancer, lung cancer, and prostate cancer were 0.129±2.185%, 0.963±2.125%, and 0.259±2.019% by the absolute method, and 0.952±2.039%, 1.704±2.080%, and 0.524±1.274% by the TG119 method. The ratio between the average of the TG119 method and the absolute method corresponded to the error of reference irradiation dose. Considering that the measurement method is simple, the TG119 method enables more stable point dose verification of VMAT.

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.

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy

PURPOSE

We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID).

METHODS

Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error.

RESULTS

The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection.

CONCLUSIONS

Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.

Effect of intensity-modulated radiation therapy on sciatic nerve injury caused by echinococcosis

Conventional radiotherapy has a good killing effect on femoral echinococcosis. However, the sciatic nerve around the lesion is irreversibly damaged owing to bystander effects. Although intensity-modulated radiation therapy shows great advantages for precise dose distribution into lesions, it is unknown whether intensity-modulated radiation therapy can perfectly protect the surrounding sciatic nerve on the basis of good killing of femoral echinococcosis foci. Therefore, this study comparatively analyzed differences between intensity-modulated radiation therapy and conventional radiotherapy on the basis of safety to peripheral nerves. Pure-breed Meriones meridiani with bilateral femoral echinococcosis were selected as the research object. Intensity-modulated radiation therapy was used to treat left femoral echinococcosis of Meriones meridianus, while conventional radiotherapy was used to treat right femoral echinococcosis of the same Meriones meridianus. The total radiation dose was 40 Gy. To understand whether intensity-modulated radiation therapy and conventional radiotherapy can kill femoral echinococcosis, trypan blue staining was used to detect pathological changes of bone Echinococcus granulosus and protoscolex death after radiotherapy. Additionally, enzyme histochemical staining was utilized to measure acid phosphatase activity in the protoscolex after radiotherapy. One week after radiotherapy, the overall structure of echinococcosis in bilateral femurs of Meriones meridiani treated by intensity-modulated radiation therapy disappeared. There was no significant difference in the mortality rate of protoscoleces of Echinococcus granulosus between the bilateral femurs of Meriones meridiani. Moreover, there was no significant difference in acid phosphatase activity in the protoscolex of Echinococcus granulosus between bilateral femurs. To understand the injury of sciatic nerve surrounding the foci of femoral echinococcosis caused by intensity-modulated radiation therapy and conventional radiotherapy, the ultrastructure of sciatic nerves after radiotherapy was observed by transmission electron microscopy. Additionally, apoptosis of neurons was examined using a terminal-deoxynucleotidyl transferase-mediated dUTP nick end labeling assay, and expression of Bcl-2 and Bax in sciatic nerve tissue was detected by immunohistochemical staining and western blot assay. Our results showed that most neurons in the left sciatic nerve of Meriones meridiani with echinococcosis treated by intensity-modulated radiation therapy had reversible injury, and there was no obvious apoptosis. Compared with conventional radiotherapy, the number of apoptotic cells and Bax expression in sciatic nerve treated by intensity-modulated radiation therapy were significantly decreased, while Bcl-2 expression was significantly increased. Our findings suggest that intensity-modulated radiation therapy has the same therapeutic effect on echinococcosis as conventional radiotherapy, and can reduce apoptosis of the sciatic nerve around foci caused by radiotherapy. Experiments were approved by the Animal Ethics Committee of People’s Hospital of Xinjiang Uygur Autonomous Region, China (Approval No. 20130301A41) on March 1, 2013.