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Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity

OBJECTIVE: To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classifica...

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Autores principales: Bin, Shizhen, Zhang, Ji, Shen, Luyao, Zhang, Junjun, Wang, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401596/
https://www.ncbi.nlm.nih.gov/pubmed/37546404
http://dx.doi.org/10.3389/fonc.2023.1094927
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author Bin, Shizhen
Zhang, Ji
Shen, Luyao
Zhang, Junjun
Wang, Qi
author_facet Bin, Shizhen
Zhang, Ji
Shen, Luyao
Zhang, Junjun
Wang, Qi
author_sort Bin, Shizhen
collection PubMed
description OBJECTIVE: To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). MATERIALS AND METHODS: 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. RESULTS: The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. CONCLUSION: All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.
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spelling pubmed-104015962023-08-05 Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity Bin, Shizhen Zhang, Ji Shen, Luyao Zhang, Junjun Wang, Qi Front Oncol Oncology OBJECTIVE: To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). MATERIALS AND METHODS: 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. RESULTS: The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. CONCLUSION: All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency. Frontiers Media S.A. 2023-07-21 /pmc/articles/PMC10401596/ /pubmed/37546404 http://dx.doi.org/10.3389/fonc.2023.1094927 Text en Copyright © 2023 Bin, Zhang, Shen, Zhang and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Bin, Shizhen
Zhang, Ji
Shen, Luyao
Zhang, Junjun
Wang, Qi
Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title_full Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title_fullStr Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title_full_unstemmed Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title_short Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
title_sort study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401596/
https://www.ncbi.nlm.nih.gov/pubmed/37546404
http://dx.doi.org/10.3389/fonc.2023.1094927
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