Systematic method for a deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance of volumetric modulated arc therapy
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- Seiji Tomori
- Department of Radiology National Hospital Organization Sendai Medical Center Sendai Miyagi 983‐8520 Japan
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- Noriyuki Kadoya
- Department of Radiation Oncology Tohoku University Graduate School of Medicine Sendai Miyagi 980‐8574 Japan
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- Tomohiro Kajikawa
- Department of Radiation Oncology Tohoku University Graduate School of Medicine Sendai Miyagi 980‐8574 Japan
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- Yuto Kimura
- Department of Radiation Oncology Tohoku University Graduate School of Medicine Sendai Miyagi 980‐8574 Japan
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- Kakutarou Narazaki
- Department of Radiology National Hospital Organization Sendai Medical Center Sendai Miyagi 983‐8520 Japan
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- Takahiro Ochi
- Department of Radiology National Hospital Organization Sendai Medical Center Sendai Miyagi 983‐8520 Japan
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- Keiichi Jingu
- Department of Radiation Oncology Tohoku University Graduate School of Medicine Sendai Miyagi 980‐8574 Japan
抄録
<jats:sec><jats:title>Purpose</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our results suggest that the training of the deep learning‐based quality assurance model can be performed using a dummy target plan.</jats:p></jats:sec>
収録刊行物
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- Medical Physics
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Medical Physics 48 (3), 1003-1018, 2021-01-28
Wiley
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キーワード
詳細情報 詳細情報について
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- CRID
- 1361981470041714560
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- DOI
- 10.1002/mp.14682
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- ISSN
- 24734209
- 00942405
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- データソース種別
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- Crossref