TY - JOUR
T1 - Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning
AU - Mehrens, Hunter
AU - Molineu, Andrea
AU - Hernandez, Nadia
AU - Court, Laurence
AU - Howell, Rebecca
AU - Jaffray, David
AU - Peterson, Christine B.
AU - Pollard-Larkin, Julianne
AU - Kry, Stephen F.
N1 - Funding Information:
National Institute of Health/National Cancer Institute (#CA180803, #CA214526 to S.K.). CBP is partially supported by NIH/NCI CCSG grant P30CA016672 (Biostatistics shared resource).
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - IROC
KW - Machine learning
KW - Phantoms
KW - Plan Complexity
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U2 - 10.1016/j.radonc.2023.109577
DO - 10.1016/j.radonc.2023.109577
M3 - Article
C2 - 36841341
AN - SCOPUS:85149357970
SN - 0167-8140
VL - 182
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
M1 - 109577
ER -