Application of failure mode and effects analysis to optimization of linac quality controls protocol

AAPM Medical Physics Practice Guideline 8.b: Linear accelerator performance tests

The purpose of this guideline is to provide a list of critical performance tests to assist the Qualified Medical Physicist (QMP) in establishing and maintaining a safe and effective quality assurance (QA) program. The performance tests on a linear accelerator (linac) should be selected to fit the clinical patterns of use of the accelerator and care should be given to perform tests which are relevant to detecting errors related to the specific use of the accelerator. Current recommendations for linac QA were reviewed to determine any changes required to those tests highlighted by the original report as well as considering new components of the treatment process that have become common since its publication. Recommendations are made on the acquisition of reference data, routine establishment of machine isocenter, basing performance tests on clinical use of the linac, working with vendors to establish QA tests and performing tests after maintenance and upgrades. The recommended tests proposed in this guideline were chosen based on consensus of the guideline's committee after assessing necessary changes from the previous report. The tests are grouped together by class of test (e.g., dosimetry, mechanical, etc.) and clinical parameter tested. Implementation notes are included for each test so that the QMP can understand the overall goal of each test. This guideline will assist the QMP in developing a comprehensive QA program for linacs in the external beam radiation therapy setting. The committee sought to prioritize tests by their implication on quality and patient safety. The QMP is ultimately responsible for implementing appropriate tests. In the spirit of the report from American Association of Physicists in Medicine Task Group 100, individual institutions are encouraged to analyze the risks involved in their own clinical practice and determine which performance tests are relevant in their own radiotherapy clinics.

Determining the quality control frequency of an MR-linac using risk matrix (RM) analysis

PURPOSE

Quality control (QC) is performed routinely through professional guidelines. However, the recommended QC frequency may not be optimal among different institutional settings. Here we propose a novel method for determining the optimal QC frequency using risk matrix (RM) analysis.

METHODS AND MATERIALS

A newly installed Magnetic Resonance linac (MR-linac) was chosen as the testing platform and six routine QC items were investigated. Failures of these QC items can adversely affect treatment outcome for the patient. Accordingly, each QC item with its assigned frequency forms a unique failure mode (FM). Using FM-effect analysis (FMEA), the severity (S), occurrence (O), and detection (D) of each FM was obtained. Next, S and D based on RM was used to determine the appropriate QC frequency. Finally, the performance of new frequency for each QC item was evaluated using the metric E = O/D.

RESULTS

One new QC frequency was the same as the old frequency, two new QC frequencies were less than the old ones, and three new QC frequencies were higher than the old ones. For six QC items, E values at the new frequencies were not less than their values at the old frequencies. This indicates that the risk of machine failure is reduced at the new QC frequencies.

CONCLUSIONS

The application of RM analysis provides a useful tool for determining the optimal frequencies for routine linac QC. This study demonstrated that linac QC can be performed in a way that maintains high performance of the treatment machine in a radiotherapy clinic.

Application of failure mode and effects analysis to validate a novel hybrid Linac QC program that integrates automated and conventional QC testing

A hybrid quality control (QC) program was developed that integrates automated and conventional Linac QC, realizing the benefits of both automated and conventional QC, increasing efficiency and maintaining independent measurement methods. Failure mode and effects analysis (FMEA) was then applied in order to validate the program prior to clinical implementation. The hybrid QC program consists of automated QC with machine performance check and DailyQA3 array on the TrueBeam Linac, and Delta4 volumetric modulated arc therapy (VMAT) standard plan measurements, alongside conventional monthly QC at a reduced frequency. The FMEA followed the method outlined in TG-100. Process maps were created for each treatment type at our center: VMAT, stereotactic body radiotherapy (SBRT), conformal, and palliative. Possible failure modes were established by evaluating each stage in the process map. The FMEA followed semiquantitative methods, using data from our QC records from eight Linacs over 3 years for the occurrence estimates, and simulation of failure modes in the treatment planning system, with scoring surveys for severity and detectability. The risk priority number (RPN) was calculated from the product of the occurrence, severity, and detectability scores and then normalized to the maximum and ranked to determine the most critical failure modes. The highest normalized RPN values (100, 90) were found to be for MLC position dynamic for both VMAT and SBRT treatments. The next highest score was 35 for beam position for SBRT, and the majority of scores were less than 20. Overall, these RPN scores for the hybrid Linac QC program indicated that it would be acceptable, but the high RPN score associated with the dynamic MLC failure mode indicates that it would be valuable to perform more rigorous testing of the MLC. The FMEA proved to be a useful tool in validating hybrid QC.

Failure modes in stereotactic radiosurgery. A narrative review

OBJECTIVES

Stereotactic radiosurgery (SRS) refers to an advanced radiotherapy technique that requires a high level of precision and accuracy and a flawless workflow. Failures within the SRS process can lead to serious consequences due to high doses delivered per treatment. This narrative review aimed to identify the riskiest failure modes (FMs) and the stages at which they occur in the SRS process, as well as the strategies applied to mitigate the risks. It was based on the analysis of published failure mode and effects analysis (FMEA) data.

KEY FINDINGS

From the literature search in PubMed and Scopus, 7 articles met the eligibility criteria for inclusion in the qualitative synthesis. In total, 9 radiotherapy departments conducted FMEA in the SRS process. 4 of them were community hospitals and 5 were academic centers. Overall, 54 high-risk FMs were identified with treatment planning (FMs: 18), treatment delivery (FMs: 12), consultation and patient registration (FMs: 10) being the riskiest stages. 10 FMs were stereotactic specific, while the remaining 44 could be met in any radiotherapy technique. Failures associated with contouring, medical records review, target reirradiation, and patient positioning were mostly outlined. Risk mitigation strategies included timeouts, double-checks, checklists, training and changes in the working practice.

CONCLUSION

Our review demonstrated that crucial FMs can occur in all SRS stages. Although generalisations were challenging, the FMs analysis provided a significant source of information about potential high risks and continuous improvement strategies that can be applied both in the SRS and other radiotherapy processes.

IMPLICATIONS FOR PRACTICE

The results of this research will assist radiotherapy facilities in proactive risk management studies and will allow radiotherapy professionals to reflect on their practice and learn from others' experiences.

Dosimetric effects of quality assurance-related setup errors in passive proton therapy for prostate cancer with and without a hydrogel spacer

The purpose of this study was to evaluate the effect of quality assurance (QA)-related setup errors in passive proton therapy for prostate cancer with and without a hydrogel spacer. We used 20 typical computed tomography (CT) images of prostate cancer: 10 patients with and 10 patients without spacers. The following 12 model errors were assumed: output error ± 2%, range error ± 1 mm, setup error ± 1 mm for three directions, and multileaf collimator (MLC) position error ± 1 mm. We created verification plans with model errors and compared the prostate-rectal (PR) distance and dose indices with and without the spacer. The mean PR distance at the isocenter was 1.1 ± 1.3 mm without the spacer and 12.9 ± 2.9 mm with the spacer (P < 0.001). The mean rectum V_{53.5 GyE}, V_{50 GyE}, and V_{34.5 GyE} in the original plan were 2.3%, 4.1%, and 12.1% without the spacer and 0.1%, 0.4%, and 3.3% with the spacer (P = 0.0011, < 0.001, and < 0.001). The effects of the range and lateral setup errors were small; however, the effects of the vertical/long setup and MLC error were significant in the cases without the spacer. The means of the maximum absolute change from original plans across all scenarios in the rectum V_{53.5 GyE}, V_{50 GyE}, and V_{34.5 GyE} were 1.3%, 1.5%, and 2.3% without the spacer, and 0.2%, 0.4%, and 1.3% with the spacer (P < 0.001, < 0.001, and = 0.0019). This study indicated that spacer injections were also effective in reducing the change in the rectal dose due to setup errors.

A patient risk model to determine the optimal output constancy check frequency for a radiotherapy machine

PURPOSE

The output constancy check, a basic quality control (QC) item for radiotherapy machines, is performed daily according to suggestions in technical reports by experienced experts. In this study, a patient risk model was built to determine the optimal frequency of an output constancy check for a specific radiotherapy machine.

METHODS AND MATERIALS

The method was based on the patient risk model and comprised three steps: 1) the power function graph was used to select a proper QC rule and the average number of QC measurements per QC rule evaluation. 2) The optimal QC frequency was determined by the minimum integer value of expected patients treated between QC measurements. 3) The individual control chart (I-Chart) was used to evaluate the effectiveness of the model. The model was implemented on the output constancy check of a Tomotherapy machine.

RESULTS

The QC rule with the limits set to the mean ± 3 standard deviations and 5 measurements per QC were selected according to the power function graph. The optimal frequency was observed every 21 patients. The I-Chart showed that the optimal frequency detected the machine failure earlier compared to the conventional daily frequency. The model could monitor whether Tomotherapy machine was in good condition and predicted the time to adjust the machine.

CONCLUSIONS

The optimal output constancy check frequency of a radiotherapy machine is determined by the number of patients, which uses patient risk model. The optimal frequency is superior to the conventional daily frequency in identifying machine failure earlier.