Power Transformations On Multivariate Non-Normal Radiotherapy Plan Quality Measures
R Widjaja1*, A Roy1, M Gopalakrishnan2, B Mittal2, (1) The University of Texas at San Antonio, San Antonio, TX, (2) Northwestern Memorial Hospital, Chicago, IL
Presentations
WE-D-TRACK 1-4 (Wednesday, 7/28/2021) 2:00 PM - 3:00 PM [Eastern Time (GMT-4)]
Purpose: To investigate the application of power transformations on skewed plan quality measures used by multivariate quality control charts in radiotherapy.
Methods: We compute multivariate risk-adjusted control charts for treatment plan quality control, which employ the resulting residuals of a risk-adjustment model that uses a set of organ-at-risk (OAR) dose-volume histogram (DVH) points as proxies for plan quality. The risk-adjustment model adjusts the DVH points for a set of patient- and treatment-specific risk factors. Given an ordinary least squares (OLS) regression-based risk-adjustment model, an assumption is that the data is normally distributed, which is typically not the case for DVH data. Thus, we employ the Box-Cox transformation that is a power transformation to reduce the impact of skewness in the DVH points. The control charts are evaluated on 69 head-and-neck cases with the brainstem serving as the primary OAR.
Results: In the multivariate control chart with Box-Cox transformation, two patients (43 and 49) are signaled as out-of-control (OC), while the chart without Box-Cox transformation signals three patients (24, 43, and 49) as OC. The DVHs show that patient 49 is a high-quality plan, while patients 24 and 43 are poor quality plans with high brainstem dose. Replanning led to a sizable improvement in patient 43’s brainstem dose while patient 24 could not be improved without sacrificing tumor dose criteria. The risk-adjustment model and the control chart fails to account for the skewed DVH data without the Box-Cox transformation, leading to a false signal for patient 24.
Conclusion: A power transformation is necessary for non-normal DVH data for statistical modeling and quality control. The proposed Box-Cox transformation can scale and adjust the skewness of DVH data, leading to an improved performance of control charts for quality control in radiotherapy.
Handouts
Keywords
Statistical Analysis, Quality Control, Transformation
Taxonomy
IM/TH- Formal Quality Management Tools: Sensitivity and statistical process control analyses
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