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

Sangutid Thongsawad; Somyot Srisatit; Todsaporn Fuangrod

doi:10.1002/acm2.13622

journal article Open Access May 18, 2022

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

Sangutid Thongsawad Somyot Srisatit Todsaporn Fuangrod

Journal of Applied Clinical Medical Physics Vol. 23 No. 7 · Wiley

View at Publisher Save 10.1002/acm2.13622

Abstract

AbstractThe 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).

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Details

Published: May 18, 2022
Vol/Issue: 23(7)
License: View

Authors

S

Sangutid Thongsawad

Department of Nuclear Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand; Princess Srisavangavadhana College of Medicine Chulabhorn Royal Academy Bangkok Thailand

S

Somyot Srisatit

Department of Nuclear Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

T

Todsaporn Fuangrod

Princess Srisavangavadhana College of Medicine Chulabhorn Royal Academy Bangkok Thailand

Cite This Article

Sangutid Thongsawad, Somyot Srisatit, Todsaporn Fuangrod (2022). 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. Journal of Applied Clinical Medical Physics, 23(7). https://doi.org/10.1002/acm2.13622

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

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