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. 2023 May:182:109577.
doi: 10.1016/j.radonc.2023.109577. Epub 2023 Feb 24.

Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning

Hunter Mehrens  1 , Andrea Molineu  2 , Nadia Hernandez  3 , Laurence Court  4 , Rebecca Howell  4 , David Jaffray  5 , Christine B Peterson  6 , Julianne Pollard-Larkin  4 , Stephen F Kry  7
Affiliations

Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning

Hunter Mehrens et al. Radiother Oncol. 2023 May.

Abstract

Aim of the study: To elucidate the important factors and their interplay that drive performance on IMRT phantoms from the Imaging and Radiation Oncology Core (IROC).

Methods: IROC's IMRT head and neck phantom contains two targets and an organ at risk. Point and 2D dose are measured by TLDs and film, respectively. 1,542 irradiations between 2012-2020 were retrospectively analyzed based on output parameters, complexity metrics, and treatment parameters. Univariate analysis compared parameters based on pass/fail, and random forest modeling was used to predict output parameters and determine the underlying importance of the variables.

Results: The average phantom pass rate was 92% and has not significantly improved over time. The step-and-shoot irradiation technique had significantly lower pass rates that significantly affected other treatment parameters' pass rates. The complexity of plans has significantly increased with time, and all aperture-based complexity metrics (except MCS) were associated with the probability of failure. Random forest-based prediction of failure had an accuracy of 98% on held-out test data not used in model training. While complexity metrics were the most important contributors, the specific metric depended on the set of treatment parameters used during the irradiation.

Conclusion: With the prevalence of errors in radiotherapy, understanding which parameters affect treatment delivery is vital to improve patient treatment. Complexity metrics were strongly predictive of irradiation failure; however, they are dependent on the specific treatment parameters. In addition, the use of one complexity metric is insufficient to monitor all aspects of the treatment plan.

Keywords: IROC; Machine learning; Phantoms; Plan Complexity.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1:
Figure 1:
IROC’s head and neck phantom made from high-impact poly. The phantom contains a semispherical primary target made of solid water (brown crescent) with four double-loaded TLDs with a nearby OAR and a secondary primary target (brown circle) with two double-loaded TLDs and two planes, axial and sagittal, of GAFchromic film.
Figure 2:
Figure 2:
a) Pass rates and number of irradiations for the head and neck phantom versus irradiation year with an overall average pass rate of 92% and no significant trend b) Average TLD values and gamma pass rates versus irradiation year. There has been an overall increased agreement between measured TLD dose and TPS calculation, but a decrease in pixels passing gamma. Linear regression performed was based on yearly averages.
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