Asset Details
Do you wish to reserve the book?
Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study
Do you wish to request the book?
Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Predicting gamma evaluation results of patient‐specific head and neck volumetric‐modulated arc therapy quality assurance based on multileaf collimator patterns and fluence map features: A feasibility study
2022
Overview
The purpose of this study was to develop a predictive model for patient‐specific VMAT QA results using multileaf collimator (MLC) effect and texture analysis. The MLC speed, acceleration and texture analysis features were extracted from 106 VMAT plans as predictors. Gamma passing rate (GPR) was collected as a response class with gamma criteria of 2%/2 mm and 3%/2 mm. The model was trained using two machine learning methods: AdaBoost classification and bagged regression trees model. GPR was classified into the “PASS” and “FAIL” for the classification model using the institutional warning level. The accuracy of the model was assessed using sensitivity and specificity. In addition, the accuracy of the regression model was determined using the difference between predicted and measured GPR. For the AdaBoost classification model, the sensitivity/specificity was 94.12%/100% and 63.63%/53.13% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. For the bagged regression trees model, the sensitivity/specificity was 94.12%/91.89% and 61.18%/68.75% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The root mean square error (RMSE) of difference between predicted and measured GPR was found at 2.44 and 1.22 for gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The promising result was found at tighter gamma criteria 2%/2 mm with 94.12% sensitivity (both bagged regression trees and AdaBoost classification model) and 100% specificity (AdaBoost classification model).
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc
