Systematic method for a deep learning-based prediction model for gamma evaluation in patient-specific quality assurance of volumetric modulated arc therapy.
IMRT
VMAT
deep learning
quality assurance
radiation therapy
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
revised:
22
11
2020
received:
15
06
2020
accepted:
09
12
2020
pubmed:
29
12
2020
medline:
1
5
2021
entrez:
28
12
2020
Statut:
ppublish
Résumé
This study aimed to develop and evaluate a novel strategy for establishing a deep learning-based gamma passing rate (GPR) prediction model for volumetric modulated arc therapy (VMAT) using dummy target plan data, one measurement process, and a multicriteria prediction method. A total of 147 VMAT plans were used for the training set (two sets of 48 dummy target plans) and test set (51 clinical target plans). The dummy plans were measured using a diode array detector. We developed an original convolutional neural network that accepts coronal and sagittal dose distributions to predict the GPRs of 36 pairs of gamma criteria from 0.5%/0.5 mm to 3%/3 mm. Sixfold cross-validation and model averaging were performed, and the mean training result and mean test result were derived from six trained models that were produced during cross-validation. Strong or moderate correlations were observed between the measured and predicted GPRs in all criteria. The mean absolute errors and root mean squared errors of the test set (clinical target plan) were 0.63 and 1.11 in 3%/3 mm, 1.16 and 1.73 in 3%/2 mm, 1.96 and 2.66 in 2%/2 mm, 5.00 and 6.35 in 1%/1 mm, and 5.42 and 6.78 in 0.5%/1 mm, respectively. The Pearson correlation coefficients were 0.80 in the training set and 0.68 in the test set at the 0.5%/1 mm criterion. Our results suggest that the training of the deep learning-based quality assurance model can be performed using a dummy target plan.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1003-1018Informations de copyright
© 2020 American Association of Physicists in Medicine.
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