ESTRO 2020 Abstract book

S79 ESTRO 2020

SP-0151 Statistical Process Control for the analysis of IGRT data R. Louwe 1 1 Wellington Blood & Cancer Center, Radiation Oncology, Wellington, New Zealand Abstract text Statistical Process Control (SPC) is a method that was developed to monitor the stability of manufacturing processes, and provides a means to decide whether or not to investigate outliers in a consistent and time/cost- efficient way. SPC has increasingly been used in radiation oncology during the last decade, predominantly to monitor the results of treatment machine quality control (QC) and patient-specific treatment plan QC. However, only a few studies describing applications of SPC in image-guided radiotherapy (IGRT) are available in literature. Current SPC applications in IGRT include the verification of image registration consistency, monitoring patient deformation, and assessing process changes and the accuracy of patient positioning over time. These studies all employed univariate SPC charts both for individual patient applications and for larger patient cohorts. However, when large data sets are analysed that include many patients and multiple quality metrics for each patient, the data may be correlated. Multivariate SPC charts, such as the multivariate generalisation of the exponentially weighted moving average (EWMA) chart, take these correlations into account and may be well suited for the analysis of ‘big data’ in IGRT, but this is currently still uncharted territory. This presentation will explore examples of multivariate SPC charts including a re-analysis of previously published results (Figure 1), and will discuss potential benefits and additional requirements of this

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