HyperArc multiple brain metastases report

This report presents the initial clinical experience with HyperArc, a novel modality that incorporates a non-coplanar, arc-based multileaf collimator (MLC) and automated treatment optimization and dose delivery. The study focuses on a patient who had previously received whole-brain radiotherapy. The effectiveness and challenges of HyperArc were assessed by evaluating various quality indices for stereotactic radiosurgery within the RTOG protocol, as well as an additional measure of toxicity in the form of the V12Gy volume. The HyperArc plan achieved quality indices of 1.13, 4.58, and 0.88 for CI, GI, and CIPaddick, respectively. The mean ICRU83 value was 0.17�01, and it remained consistent across all six lesions. The V12Gy value was equal to 8.76 cc. The HyperArc plan successfully met the constraints for organs-at-risk (OAR). These results suggest that HyperArc is a suitable modality for treating multiple brain metastases, as indicated by the quality indices and metrics. Additionally, V12Gy is a valuable indicator for assessing low-dose spillage.

Forecasting machine performance check output using Holt-Winters approach

Background: Machine Performance Check (MPC) is an automated TrueBeam quality control (QC) tool used to verify beam output, isocenter, and uniformity. The aim of this study was to build an MPC output variation time series modeled on the Holt-Winters method over thirty days. Methods: After AAPM TG-51 and baseline data were established for the Edge TrueBeam, daily MPC output data were gathered and analyzed through a Holt-Winters (additive and multiplicative) method. The model's performance was assessed via three standard error measures: the mean squared error (MSE), the mean absolute percentage error (MAPE), and the mean absolute deviation (MAE). The aim was achieved using a nonlinear multistart solver on the Excel platform. Results: The results showed that MPC output variation forecasting is energy and model dependent. Both additive and multiplicative Holt-Winters methods were suitable for the analysis. The performance metrics MSE, MAPE, and MAD were found to be well within acceptable limits. Conclusions: A Holt-Winters model was able to accurately forecast the MPC output variation.

Statistical process control: machine performance check output variation

Background: The aim of this study was to illustrate and evaluate the use of different statistical process control (SPC) aspects to examine linear accelerator daily output variation through machine performance check (MPC) over a month. Methods: MPC daily output data were obtained over a month after AAPM TG-51 were performed. Baseline data were set, and subsequent data were conducted through SPC. The Shewhart chart was used to determine the upper and lower control limits, whereas CUSUM for subtle changes. Results: The upper and lower control limits obtained via SPC analysis of the MPC data were found to fall within AAPM Task Group 142 guidelines. MPC output variation data were within ±3% of their action limits values and were within 1% over thirty days of data. The process capability ratio and process acceptability ratio, Cp and Cpk values were ?2 for all energies. Potential undetected deviations were captured by the CUSUM chart for photons and electrons beam energy. Conclusions: Control charts were found to be useful in terms of detecting changes in MPC output.