Application and comparison of machine learning models for predicting quality assurance outcomes in radiation therapy treatment planning
Abstract The purpose of this study was to develop and evaluate machine learning models for predicting quality assurance (QA) outcomes of volumetric modulated arc radiation therapy (VMAT) treatment plans. A dataset of 500 VMAT treatment plans and diode-array QA measurements were collected for this study. Gamma passing rates (GPRs) were computed using a 3%/3 mm dose difference and distance-to-agreement gamma criterion with local normalization. 241 complexity metrics and plan parameters were extracted from each treatment plan and their relative importance for accurately predicting GPRs was assessed and compared using feature selection methods via forests of Extra-Trees, mutual information, and linear regression. Hyperparameters of different machine learning models – which included linear models, support vector machines (SVMs), tree-based models, and neural networks – were tuned using cross-validation on the training data (80%/20% training/testing split). Features were weakly correlated with GPRs, with the small aperture score (SAS) at 50 mm having the largest absolute Pearson correlation coefficient (0.38; p
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Application and comparison of machine learning models for predicting quality assurance outcomes in radiation therapy treatment planning
Semantic Scholar · Medicine · 2020
Abstract
Abstract The purpose of this study was to develop and evaluate machine learning models for predicting quality assurance (QA) outcomes of volumetric modulated arc radiation therapy (VMAT) treatment plans. A dataset of 500 VMAT treatment plans and diode-array QA measurements were collected for this study. Gamma passing rates (GPRs) were computed using a 3%/3 mm dose difference and distance-to-agreement gamma criterion with local normalization. 241 complexity metrics and plan parameters were extracted from each treatment plan and their relative importance for accurately predicting GPRs was assessed and compared using feature selection methods via forests of Extra-Trees, mutual information, and linear regression. Hyperparameters of different machine learning models – which included linear models, support vector machines (SVMs), tree-based models, and neural networks – were tuned using cross-validation on the training data (80%/20% training/testing split). Features were weakly correlated with GPRs, with the small aperture score (SAS) at 50 mm having the largest absolute Pearson correlation coefficient (0.38; p