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.

Autor: Thongsawad S; Department of Nuclear Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.; Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand., Srisatit S; Department of Nuclear Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand., Fuangrod T; Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand.
Jazyk: angličtina
Zdroj: Journal of applied clinical medical physics [J Appl Clin Med Phys] 2022 Jul; Vol. 23 (7), pp. e13622. Date of Electronic Publication: 2022 May 18.
DOI: 10.1002/acm2.13622
Abstrakt: 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).
(© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)
Databáze: MEDLINE
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