Detecting outliers beyond tolerance limits derived from statistical process control in patient‐specific quality assurance

Abstract Background Tolerance limit is defined on pre‐treatment patient specific quality assurance results to identify “out of the norm” dose discrepancy in plan. An out‐of‐tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out‐of‐tolerance plan early during treatment planning phase to mitigate the above‐mentioned risks. Methods Patient‐specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria. Statistical process control method was used to determine six different site and criterion‐specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field—robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1‐score calculated from both the inliers and validation outliers’ data. Results 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2 mm, 2%/1 mm, and 1%/1 mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1‐score of 0.72 (0.70) when using the 2%/2 mm (2%/1 mm) criterion. Conclusion The model will help the planner to identify an out‐of‐tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.