8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control

Application and Challenges of Statistical Process Control in Radiation Therapy Quality Assurance

Quality assurance (QA) is important for ensuring precision in radiation therapy. The complexity and resource-intensive nature of QA has increased with the continual evolution of equipment and techniques. An effective approach is to improve the process control technology and resource optimization. Statistical process control is an economical and efficient tool that has been widely used to monitor, control, and improve quality management processes and is now being increasingly used for radiation therapy QA. This article reviews the development and methodology of statistical process control technology, evaluates its suitability in radiation therapy QA practices, and assesses its importance and challenges in optimizing radiation therapy QA processes.

Moving towards process-based radiotherapy quality assurance using statistical process control

Monitoring Radiotherapy Quality Assurance (QA) using Statistical Process Control (SPC) methods has gained wide acceptance. The significance of understanding the SPC methodologies has increased among the medical physics community with the release of Task Group (TG) reports from the American Association of Physicists in Medicine (AAPM) on patient-specific QA (PSQA) (TG-218) and Proton therapy QA (TG-224). Even though these reports recommend using SPC for QA analysis, physicists have ambiguities and doubts in choosing proper SPC tools and methodologies. This review article summarises the utilisation of SPC methods for different Radiotherapy QAs published in the literature, such as PSQA, routine Linac QA and patient positional verification. QA analysis using SPC could assist the user in distinguishing between 'special' and 'routine' sources of variations in the QA, which can aid in reducing actions on false positive QA results. For improved PSQA monitoring, machine-specific, site-specific, and technique-specific Tolerance Limits and Action Limits derived from a two-stage SPC-based approach can be used. Adopting a combination of Shewhart's control charts and time-weighted control charts for routine Linac QA monitoring could add more insights to the QA process. Incorporating SPC tools into existing image review modules or introducing new SPC software packages specifically designed for clinical use can significantly enhance the image review process. Proper selection and having adequate knowledge of SPC tools are essential for efficient QA monitoring, which is a function of the type of QA data available, and the magnitude of process drift to be monitored.

Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance

PURPOSE

To assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes.

MATERIALS AND METHODS

A total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20-1000 were used to estimate the lower control limit (LCL). Based on the iterative "Identify-Eliminate-Recalculate" and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL₀) and false alarm rate (FAR₀) were calculated to evaluate the performance of LCL.

RESULTS

The ground truth of the values of LCL, FAR₀, and ARL₀ obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL₀ values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the "Identify-Eliminate-Recalculate" procedure, only the median LCL values obtained by the WSD method were closest to the ground truth.

CONCLUSIONS

Sampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative "Identify-Eliminate-Recalculate" procedure exhibited sufficient robustness and reliability.

Special stereotactic radiotherapy techniques: procedures and equipment for treatment simulation and dose delivery

Stereotactic radiotherapy (SRT ) is a multi-step procedure with each step requiring extreme accuracy. Physician-dependent accuracy includes appropriate disease staging, multi-disciplinary discussion with shared decision-making, choice of morphological and functional imaging methods to identify and delineate the tumor target and organs at risk, an image-guided patient set-up, active or passive management of intra-fraction movement, clinical and instrumental follow-up. Medical physicist-dependent accuracy includes use of advanced software for treatment planning and more advanced Quality Assurance procedures than required for conventional radiotherapy. Consequently, all the professionals require appropriate training in skills for high-quality SRT.

Thanks to the technological advances, SRT has moved from a “frame-based” technique, i.e. the use of stereotactic coordinates which are identified by means of rigid localization frames, to the modern “frame-less” SRT which localizes the target volume directly, or by means of anatomical surrogates or fiducial markers that have previously been placed within or near the target. This review describes all the SRT steps in depth, from target simulation and delineation procedures to treatment delivery and image-guided radiation therapy. Target movement assessment and management are also described.

Total workflow uncertainty of frameless radiosurgery with the Gamma Knife Icon cone-beam computed tomography

Objective

Frameless treatment with the Gamma Knife Icon is still relatively new as a treatment option. As a result, additional confidence/knowledge about the uncertainty that exists within each portion of the treatment workflow could be gained especially regarding steps that have not been previously studied in the literature.

Methods

The Icon base delivery device (Perfexion) uncertainty is quantified and validated. The novel portions of the Icon such as mask immobilization, cone‐beam computed tomography image guidance, and the intrafraction motion management methods are studied specifically and to a greater extent to determine a total workflow uncertainty of frameless treatment with the Icon.

Results

The uncertainty of each treatment workflow step has been identified with the total workflow uncertainty being identified in this work as 1.3 mm with a standard deviation of 0.51 mm.

Conclusion

The total uncertainty of frameless treatment with the Icon has been evaluated and this data may indicate the need for setup margin in this setting with data that could be used by other institutions to calculate needed setup margin per their preferred recipe after validation of this data in their context.