Performance assessment of surface-guided radiation therapy and patient setup in head-and-neck and breast cancer patients based on statistical process control

PurposeTo assess the effectiveness of SGRT in clinical applications through statistical process control (SPC).MethodsTaking the patients’ positioning through optical surface imaging (OSI) as a process, the average level of process execution was defined as the process mean. Setup errors detected by cone-beam computed tomography (CBCT) and OSI were extracted for head-and-neck cancer (HNC) and breast cancer patients. These data were used to construct individual and exponentially weighted moving average (EWMA) control charts to analyze outlier fractions and small process shifts from the process mean. Using the control charts and process capability indices derived from this process, the patient positioning-related OSI performance and setup error were analyzed for each patient.ResultsOutlier fractions and small shifts from the process mean that are indicative of setup errors were found to be widely prevalent, with the outliers randomly distributed between fractions. A systematic error of up to 1.6 mm between the OSI and CBCT results was observed in all directions, indicating a significantly degraded OSI performance. Adjusting this systematic error for each patient using setup errors of the first five fractions could effectively mitigate these effects. Process capability analysis following adjustment for systematic error indicated that OSI performance was acceptable (process capability index C_pk = 1.0) for HNC patients but unacceptable (C_pk < 0.75) for breast cancer patients.ConclusionSPC is a powerful tool for detecting the outlier fractions and process changes. Our application of SPC to patient-specific evaluations validated the suitability of OSI in clinical applications involving patient positioning.     

A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean

The usual practice of using a control chart to monitor a process is to take samples from the process with fixed sampling interval (FSI). In this paper, a synthetic X¯ control chart with the variable sampling interval (VSI) feature is proposed for monitoring changes in the process mean. The VSI synthetic X¯ chart integrates the VSI X¯ chart and the VSI conforming run length (CRL) chart. The proposed VSI synthetic X¯ chart is evaluated using the average time to signal (ATS) criterion. The optimal charting parameters of the proposed chart are obtained by minimizing the out-of-control ATS for a desired shift. Comparisons between the VSI synthetic X¯ chart and the existing X¯, synthetic X¯, VSI X¯ and EWMA X¯ charts, in terms of ATS, are made. The ATS results show that the VSI synthetic X¯ chart outperforms the other X¯ type charts for detecting moderate and large shifts. An illustrative example is also presented to explain the application of the VSI synthetic X¯ chart.     

Retrospective study on performance of constancy check device in Linac beam monitoring using Statistical Process Control

AimTo examine the application of Statistical Process Control (SPC) and Ishikawa diagrams for retrospective evaluation of machine Quality Assurance (QA) performance in radiotherapyBackgroundSPC is a popular method for supplementing the performance of QA techniques in healthcare. This work investigates the applicability of SPC techniques and Ishikawa charts in machine QA.Materials and MethodsSPC has been applied to recommend QA limits on the particular beam parameters using the QUICKCHECK^webline QA portable constancy check device for 6 MV and 10 MV flattened photon beams from the Elekta Versa HD linear accelerator (Linac). Four machine QA parameters – beam flatness, beam symmetry along gun target direction and left-right direction, and beam quality factor (BQF) – were selected for retrospective analysis. Shewhart charts, Exponentially Weighted Moving Average (EWMA) charts and Cumulative Sum (CUSUM) charts were obtained for each parameter. The root causes for a failure in machine QA were broken down into an Ishikawa diagram enabling the user to identify the root cause of error and rectify the problem subsequently.ResultsShewhart charts and EWMA charts applied could detect loss in control in one variable in the 6 MV beams and in all four variables in 10 MV beams. CUSUM charts detected offsets in the readings. The Ishikawa chart exhaustively included the possible errors that lead to loss of control.ConclusionSPC is proven to be effective for detection of loss in control in machine QA. The Ishikawa chart provides the set of probable root causes of machine error useful while troubleshooting.     

A vector autoregressive moving average time series approach for describing asymmetries of antennal control of two millipede species

This article presents a statistical method, vector autoregressive moving average time series analysis, which makes no initial assumptions about the controlling interactions between variables in the data beyond those of linear systems, and has been designed to be statistically valid without requiring several repetitions of data sets. It is therefore very useful for studying physiological and behavioral phenomena.This new methodology is applied to a kinematic analysis of antennal scanning movements in two species of millipede. The analysis demonstrates features of the generation of the antennal and head movements, the direction of information flow within the central nervous system and consequent asymmetric control relationships between bilaterally homologous body parts.