Treatment plan quality control using multivariate control charts

Phase I quality control framework for monitoring organ-at-risk dose

Objective.The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling'sT2chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC).Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans.Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40's plan and updating the DVH and control chart, P40 was IC, but P14* (P14's re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans.Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

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.

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.

Automated scripting of the dosimetric evaluation of adaptive versus non-adaptive radiotherapy

Objective. To quantify the benefit of adaptive radiotherapy over non-adaptive radiotherapy it is useful to extract and compare dosimetric features of patient treatments in both scenarios. This requires Image-Guided Radiotherapy (IGRT) matching of baseline planning to adaptive fraction imaging, followed by extraction of relevant dose metrics. This can be impractical to retrospectively perform manually for multiple patients.Approach. Here we present an algorithm for automatic IGRT matching of baseline planning with fraction imaging and performing automated dosimetric feature extraction from adaptive and non-adaptive treatment plans, thereby allowing comparison of the two scenarios. This workflow can be done in an entirely automated way via scripting solutions given structure and dose Digital Imaging and Communications in Medicine (DICOM) files from baseline and adaptive fractions. We validate this algorithm against the results of manual IGRT matching. We also demonstrate automated dosimetric feature extraction. Lastly, we combine these two scripting solutions to extract daily adaptive and non-adaptive radiotherapy dosimetric features from an initial cohort of patients treated on an MRI guided linear accelerator (MR-LINAC).Results.Our results demonstrate that automated feature extraction and IGRT matching was successful and comparable to results performed by a manual operator. We have therefore demonstrated a method for easy analysis of patients treated on an adaptive radiotherapy platform.Significance.We believe that this scripting solution can be used for quantifying the benefits of adaptive therapy and for comparing adaptive therapy against various non-adaptive IGRT scenarios (e.g. 6 degree of freedom couch rotation).