Statistical process control to monitor use of a web‐based autoplanning tool

Purpose To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). Methods Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. Results For RayStation, the average mean percent dose differences across all contours were 0.65% +/- 1.05%, -2.09% +/- 0.56%, and 0.28% +/- 0.98% and average control limit ranges were 1.89% +/- 1.32%, 2.16% +/- 1.31%, and 2.65% +/- 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62% +/- 0.34%, 0.32% +/- 0.23%, and -0.91% +/- 0.98%, while average control limit ranges were 1.09% +/- 0.77%, 3.69% +/- 2.67%, 2.73% +/- 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 +/- 0.019 cm and MLC-TF value of 0.020 +/- 0.001 for Acuros and a DLG value of 0.135 +/- 0.031 cm and MLC-TF value of 0.015 +/- 0.001 for AAA. Conclusions Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.