Complexity in Radiation Therapy: It's Complicated

Kamperis, Efstathios; Kodona, Chionia; Hatziioannou, Konstantinos; Giannouzakos, Vasileios

doi:10.1016/j.ijrobp.2019.09.003

Cited by 26 publications

(20 citation statements)

References 28 publications

(37 reference statements)

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“…the aperture shape controller (ASC) tool in the Eclipse TPS [94] and the modulation factor (MF) in the TomoTherapy Hi-Art treatment system [95][96][97], but most TPSs still do not directly incorporate advanced complexity metrics [77]. Thus, unnecessary plan complexity is sometimes a consequence of inverse planning and better tools are needed to handle plan complexity [98]. To that aim, we encourage TPS manufacturers to: Minimise and better control plan complexity by incorporating complexity metrics into the optimisation algorithms.…”

Section: Plan Complexitymentioning

confidence: 99%

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Hernández

Hansen

Widesott³

et al. 2020

Radiotherapy and Oncology

124

114

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Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

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Section: Plan Complexitymentioning

confidence: 99%

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Hernández

Hansen

Widesott³

et al. 2020

Radiotherapy and Oncology

124

114

View full text Add to dashboard Cite

show abstract

“…are used to demonstrate that plans generated with a novel planning technique are not more complex than those from another conventional planning approach. However, there is a lack of agreement on the general applicability of proposed plan complexity parameters in terms of prediction of plan deliverability [19]. 41.…”

Section: Plan Deliverability and Complexitymentioning

confidence: 99%

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Hansen

Crijns

Hussein

et al. 2020

Radiotherapy and Oncology

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“…Over the past few decades, radiation treatment delivery techniques have been improving. The introductions of intensity‐modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) have provided a more conformal dose coverage to the target volumes while sparing the normal tissue and nearby organs at risk (OARs) 1,2 . VMAT provides a conformal dose to the targets and OARs through the modulation of the beam.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

Sivabhaskar

Roy

et al. 2022

J Applied Clin Med Phys

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Purpose Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans. Methods For this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R2, and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance. Results The models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045). Conclusions We examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

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Complexity in Radiation Therapy: It's Complicated

Cited by 26 publications

References 28 publications

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

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