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Systematic method for a deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance of volumetric modulated arc therapy
Author(s) -
Tomori Seiji,
Kadoya Noriyuki,
Kajikawa Tomohiro,
Kimura Yuto,
Narazaki Kakutarou,
Ochi Takahiro,
Jingu Keiichi
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14682
Subject(s) - quality assurance , test set , pearson product moment correlation coefficient , mathematics , artificial neural network , mean squared error , set (abstract data type) , artificial intelligence , computer science , nuclear medicine , statistics , pattern recognition (psychology) , medicine , external quality assessment , pathology , programming language
Purpose 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. Methods 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. Results 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. Conclusion Our results suggest that the training of the deep learning‐based quality assurance model can be performed using a dummy target plan.