Implementation of output prediction models for a passively double‐scattered proton therapy system

Ferguson, S; Ahmad, Salahuddin; Jin, H

doi:10.1118/1.4965046

Cited by 7 publications

(15 citation statements)

References 16 publications

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“…The model can serve as the secondary verification of measured OFs. In this work we built three models based on machine learning methods to predict OFs and compared with the semi‐empirical modes developed for the MGH proton machine and later implemented for other proton machines . We have concluded that all the three machine learning models provided better results in terms of maximum absolute difference than previously developed semi‐empirical model, which has a large prediction error for the proton fields with a full range and full modulation width.…”

Section: Discussionmentioning

confidence: 93%

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A machine learning approach to the accurate prediction of monitor units for a compact proton machine

et al. 2018

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Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements.

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Section: Discussionmentioning

confidence: 93%

“…Ferguson et al. implemented a similar model using different variations in the definition of R and M to predict OFs on limited data points for Mevion S250 …”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

et al. 2018

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“…The modification was necessary for more accurate output prediction due to mismatch between nominal (vendor definition), theoretical (without straggling), and actually measured values of R and M . The derivation can be found in the appendix of our previous publication …”

Section: Methodsmentioning

confidence: 99%

“…All the option‐specific parameters for models B and C are fit by the Matlab curve fitting toolbox. Implementation of the analytical models in detail can be found in our previous publication …”

Section: Methodsmentioning

confidence: 99%

“…Recently, we commissioned the correction‐based model and one of the analytical models for our compact double‐scattered S250 proton therapy system (Mevion Medical Systems, Littleton, MA, USA). In addition, a new model mathematically derived from the analytical model was commissioned. The purpose of this research is to investigate the comparability of the three output prediction models for the compact proton therapy system.…”

Section: Introductionmentioning

confidence: 99%

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Comparability of three output prediction models for a compact passively double‐scattered proton therapy system

Ferguson

Chen

Ferreira

et al. 2017

J Applied Clin Med Phys

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The purpose of this study was to investigate comparability of three output prediction models for a compact double‐scattered proton therapy system. Two published output prediction models are commissioned for our Mevion S250 proton therapy system. Model A is a correction‐based model (Sahoo et al., Med Phys, 2008;35(11):5088–5097) and model B is an analytical model which employs a function of r = (R’‐M’)/M’ (Kooy et al., Phys Med Biol, 2005;50:5487–5456) where R’ is defined as depth of distal 100% dose with straggling and M’ is the width between distal 100% dose and proximal 100% dose with straggling instead of the theoretical definition due to more accurate output prediction. The r is converted to ((R‐0.31)‐0.81 × M)/(0.81 × M) with the vendor definition of R (distal 90% dose) and M (distal 90% dose‐to‐proximal 95% dose), where R’ = R‐0.31 (g cm−2) and M’ = 0.81 × M (g cm−2). In addition, a quartic polynomial fit model (model C) mathematically converted from model B is studied. The outputs of 272 sets of R and M covering the 24 double scattering options are measured. Each model's predicted output is compared to the measured output. For the total dataset, the percent difference between predicted (P) and measured (M) outputs ((P‐M)/M × 100%) were within ±3% using the three different models. The average differences (±standard deviation) were −0.13 ± 0.94%, −0.13 ± 1.20%, and −0.22 ± 1.11% for models A, B, and C, respectively. The p‐values of the t‐test were 0.912 (model A vs. B), 0.061 (model A vs. C), and 0.136 (model B vs. C). For all the options, all three models have clinically acceptable predictions. The differences between models A, B, and C are statistically insignificant; however, model A generally has the potential to more accurately predict the output if a larger dataset for commissioning is used. It is concluded that the models can be comparably used for the compact proton therapy system.

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A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study

Fleury,

Herault,

Spruijt

et al. 2024

Phys. Med. Biol.

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Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pretreatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units predictions using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: i) decision tree; ii) random forest, iii) extra trees, iv) K-Nearest neighbors, v) gradient boosting, and vi) the support vector regression. Features used as inputs into each machine learning pipeline were: spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.

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Implementation of output prediction models for a passively double‐scattered proton therapy system

Cited by 7 publications

References 16 publications

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

Comparability of three output prediction models for a compact passively double‐scattered proton therapy system

A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study

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