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• W3201512974 abstract "PurposeTo create and investigate a novel, clinical decision-support system using machine learning (ML).Methods and MaterialsThe ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross-validation and an area-under-the-curve (AUC) metric, with the highest performing one selected for further investigation. The model predictions were explained with Shapely additive explanation (SHAP) interaction values.ResultsThe highest-performing model was Logistic Regression achieving an accuracy of 93.8 ± 4.1% and AUC of 0.98 ± 0.02 on the testing data. The SHAP analysis indicated that the ΔD99% metric for PTV had the greatest influence on the model predictions. The least important feature was ΔDMAX for the left and right cochleae.ConclusionsThe trained model achieved satisfactory accuracy and can be used by medical physicists in a data-driven quality assurance program as well as by radiation oncologists to support their decision-making process in terms of treatment plan approval and potential plan modifications. Model explanation analysis showed that the model relies on clinically valid logic when making predictions." @default.
• W3201512974 created "2021-09-27" @default.
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• W3201512974 date "2021-11-01" @default.
• W3201512974 modified "2023-10-16" @default.
• W3201512974 title "Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors" @default.
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• W3201512974 doi "https://doi.org/10.1016/j.ctro.2021.09.001" @default.
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