Predicting the likelihood of QA failure using treatment plan accuracy metrics

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Kairn, T.^1,2 ; Crowe, S.B.^2 ; Kenny, J.^3,4 ; Knight, R.T.^1 ; Trapp, J.V.^2

Premion Cancer Care, Brisbane, Australia^1

Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia^2

Australian Clinical Dosimetry Service, Australian Radiation Protection, Nuclear Safety Agency, Melbourne, Australia^3

Radiation Oncology Queensland, Toowoomba, Australia^4

关键词: Automated data processing;    Beam segments;    Simple tests;    Small aperture;    Small samples;    Software suite;    Threshold fields;    Treatment plans;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012051/pdf
DOI  :  10.1088/1742-6596/489/1/012051

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This study used automated data processing techniques to calculate a set of novel treatment plan accuracy metrics, and investigate their usefulness as predictors of quality assurance (QA) success and failure. A small sample of 151 beams from 23 prostate and cranial IMRT treatment plans were used in this study. These plans had been evaluated before treatment using measurements with a diode array system. The TADA software suite was adapted to allow automatic batch calculation of several proposed plan accuracy metrics, including mean field area, small-aperture, off-axis and closed-leaf factors. All of these results were compared to the gamma pass rates from the QA measurements and correlations were investigated. The mean field area factor provided a threshold field size (5 cm2, equivalent to a 2.2 × 2.2 cm2square field), below which all beams failed the QA tests. The small aperture score provided a useful predictor of plan failure, when averaged over all beams, despite being weakly correlated with gamma pass rates for individual beams. By contrast, the closed leaf and off-axis factors provided information about the geometric arrangement of the beam segments but were not useful for distinguishing between plans that passed and failed QA. This study has provided some simple tests for plan accuracy, which may help minimise time spent on QA assessments of treatments that are unlikely to pass.