Failure mode and effect analysis‐based quality assurance for dynamic MLC tracking systems

The purpose of this study is to evaluate patient setup accuracy and quantify individual and cumulative positioning uncertainties associated with different hardware and software components of the stereotactic radiotherapy (SRS/SRT) with the frameless 6D ExacTrac system. A statistical model is used to evaluate positioning uncertainties of the different components of SRS/SRT treatment with the Brainlab 6D ExacTrac system using the positioning shifts of 35 patients having cranial lesions. All these patients are immobilized with rigid head‐and‐neck masks, simulated with Brainlab localizer and planned with iPlan treatment planning system. Stereoscopic X‐ray images (XC) are acquired and registered to corresponding digitally reconstructed radiographs using bony‐anatomy matching to calculate 6D translational and rotational shifts. When the shifts are within tolerance (0.7 mm and 1°), treatment is initiated. Otherwise corrections are applied and additional X‐rays (XV) are acquired to verify that patient position is within tolerance. The uncertainties from the mask, localizer, IR ‐frame, X‐ray imaging, MV, and kV isocentricity are quantified individually. Mask uncertainty (translational: lateral, longitudinal, vertical; rotational: pitch, roll, yaw) is the largest and varies with patients in the range false(−2.07−3.71mm,−5.82−5.62mm,−5.84−3.61mm;−2.10−2.40∘,−2.23−2.60∘,and−2.7−3.00∘false) obtained from mean of XC shifts for each patient. Setup uncertainty in IR positioning (0.88, 2.12, 1.40 mm, and 0.64°, 0.83°, 0.96°) is extracted from standard deviation of XC. Systematic uncertainties of the frame (0.18, 0.25, −1.27mm, −0.32∘, 0.18°, and 0.47°) and localizer (−0.03, −0.01, 0.03 mm, and −0.03∘, 0.00°, −0.01∘) are extracted from means of all XV setups and mean of all XC distributions, respectively. Uncertainties in isocentricity of the MV radiotherapy machine are (0.27, 0.24, 0.34 mm) and kV imager (0.15, −0.4, 0.21 mm). A statistical model is developed to evaluate the individual and cumulative systematic and random positioning uncertainties induced by the different hardware and software components of the 6D ExacTrac system. The uncertainties from the mask, localizer, IR frame, X‐ray imaging, couch, MV linac, and kV imager isocentricity are quantified using statistical modeling.PACS number(s): 87.56.B‐, 87.59.B‐

Section: Discussionmentioning

confidence: 99%

Quantitative evaluation of patient setup uncertainty of stereotactic radiotherapy with the frameless 6D ExacTrac system using statistical modeling

Keeling

Hossain

Jin

et al. 2016

“…Second, many reported radiation therapy incidences resulted from incorrect or inappropriate use of radiation treatment devices due to miscommunication or misunderstanding rather than device failures. As such, the development of quality management programs that focuses on the design and execution of various radiotherapy processes has become a subject of significant interest in recent years 12, 13, 14, 15, 16, 17, 18, 19, 20. Following the recommendations from the AAPM task group report No.…”

Section: Introductionmentioning

confidence: 99%

Failure modes and effects analysis (FMEA) for Gamma Knife radiosurgery

Bhatnagar

Bednarz

et al. 2017

PurposeGamma Knife radiosurgery is a highly precise and accurate treatment technique for treating brain diseases with low risk of serious error that nevertheless could potentially be reduced. We applied the AAPM Task Group 100 recommended failure modes and effects analysis (FMEA) tool to develop a risk‐based quality management program for Gamma Knife radiosurgery.MethodsA team consisting of medical physicists, radiation oncologists, neurosurgeons, radiation safety officers, nurses, operating room technologists, and schedulers at our institution and an external physicist expert on Gamma Knife was formed for the FMEA study. A process tree and a failure mode table were created for the Gamma Knife radiosurgery procedures using the Leksell Gamma Knife Perfexion and 4C units. Three scores for the probability of occurrence (O), the severity (S), and the probability of no detection for failure mode (D) were assigned to each failure mode by 8 professionals on a scale from 1 to 10. An overall risk priority number (RPN) for each failure mode was then calculated from the averaged O, S, and D scores. The coefficient of variation for each O, S, or D score was also calculated. The failure modes identified were prioritized in terms of both the RPN scores and the severity scores.ResultsThe established process tree for Gamma Knife radiosurgery consists of 10 subprocesses and 53 steps, including a subprocess for frame placement and 11 steps that are directly related to the frame‐based nature of the Gamma Knife radiosurgery. Out of the 86 failure modes identified, 40 Gamma Knife specific failure modes were caused by the potential for inappropriate use of the radiosurgery head frame, the imaging fiducial boxes, the Gamma Knife helmets and plugs, the skull definition tools as well as other features of the GammaPlan treatment planning system. The other 46 failure modes are associated with the registration, imaging, image transfer, contouring processes that are common for all external beam radiation therapy techniques. The failure modes with the highest hazard scores are related to imperfect frame adaptor attachment, bad fiducial box assembly, unsecured plugs/inserts, overlooked target areas, and undetected machine mechanical failure during the morning QA process.ConclusionsThe implementation of the FMEA approach for Gamma Knife radiosurgery enabled deeper understanding of the overall process among all professionals involved in the care of the patient and helped identify potential weaknesses in the overall process. The results of the present study give us a basis for the development of a risk based quality management program for Gamma Knife radiosurgery.

“…Data have been published on FMEA for stereotactic radiosurgery (SRS),8, 9, 10 stereotactic body radiation therapy (SBRT),11, 12 tomotherapy,13 intraoperative radiation therapy (IORT),14 treatment planning,15 and dynamic MLC tracking 16. These valuable publications primarily come from large institutions where staffing levels may allow for variable amounts of time allocation for projects of this magnitude.…”

Section: Introductionmentioning

confidence: 99%

Failure mode and effects analysis: A community practice perspective

Schuller

Burns

Ceilley

et al. 2017

PurposeTo report our early experiences with failure mode and effects analysis (FMEA) in a community practice setting.MethodsThe FMEA facilitator received extensive training at the AAPM Summer School. Early efforts focused on department education and emphasized the need for process evaluation in the context of high profile radiation therapy accidents. A multidisciplinary team was assembled with representation from each of the major department disciplines. Stereotactic radiosurgery (SRS) was identified as the most appropriate treatment technique for the first FMEA evaluation, as it is largely self‐contained and has the potential to produce high impact failure modes. Process mapping was completed using breakout sessions, and then compiled into a simple electronic format. Weekly sessions were used to complete the FMEA evaluation. Risk priority number (RPN) values > 100 or severity scores of 9 or 10 were considered high risk. The overall time commitment was also tracked.ResultsThe final SRS process map contained 15 major process steps and 183 subprocess steps. Splitting the process map into individual assignments was a successful strategy for our group. The process map was designed to contain enough detail such that another radiation oncology team would be able to perform our procedures. Continuous facilitator involvement helped maintain consistent scoring during FMEA. Practice changes were made responding to the highest RPN scores, and new resulting RPN scores were below our high‐risk threshold. The estimated person‐hour equivalent for project completion was 258 hr.ConclusionsThis report provides important details on the initial steps we took to complete our first FMEA, providing guidance for community practices seeking to incorporate this process into their quality assurance (QA) program. Determining the feasibility of implementing complex QA processes into different practice settings will take on increasing significance as the field of radiation oncology transitions into the new TG‐100 QA paradigm.