2020
DOI: 10.1002/mp.14374
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Using deep learning to predict beam‐tunable Pareto optimal dose distribution for intensity‐modulated radiation therapy

Abstract: Purpose: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep lear… Show more

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Cited by 23 publications
(22 citation statements)
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“…As shown in Table 6 , three investigations on prostate cancer have been reported for predicting pareto optimal dose distributions. 137 , 138 , 147 For each patient in the training set, 10, 100, and 500 plans were generated by Ma et al Nguyen et al and Bohara et al respectively, to sample the pareto surface with different tradeoffs. An optimal number of plans per patient in training set is unknown as it may depend on case to case basis.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…As shown in Table 6 , three investigations on prostate cancer have been reported for predicting pareto optimal dose distributions. 137 , 138 , 147 For each patient in the training set, 10, 100, and 500 plans were generated by Ma et al Nguyen et al and Bohara et al respectively, to sample the pareto surface with different tradeoffs. An optimal number of plans per patient in training set is unknown as it may depend on case to case basis.…”
Section: Resultsmentioning
confidence: 99%
“…To address this clinical tradeoff problem across different dose distributions, Nguyen et al proposed the differentiable loss function based on the DVH and adversarial loss in addition to traditional voxel-wise mean square error (MSE) loss to train the network. 147 Along the same line of work, Bohara et al incorporated beam information to predict pareto dose distribution using anatomy beam model proposed by Barragán-Montero et al 138 U-Net architecture has also been used for radiopharmaceutical dosimetry. 134,140 The network was trained to predict 3D dose rate maps given the mass density distribution and radioactivity maps.…”
Section: Overview Of Cnn-based Workmentioning
confidence: 99%
“…While it is possible to estimate dose distribution using algorithms such as deformable image registration (50), most recent dose prediction algorithms are based on ML or DL models (51). Categorized by prediction algorithms, dose distribution could be predicted by shallow ML models (49,51), deep neural network models such as the convolutional neural network (CNN) (47,(52)(53)(54)(55)(56)(57)(58)(59) and the generative adversarial network (GAN) (48,60,61). Categorized by input/output dimensions, dose distribution could be predicted voxel by voxel (49,51), slice by slice (47,48,52,56,59,62), or as a 3D volume (53,54,57,58,60,61).…”
Section: Kbp Dvh Predictionmentioning
confidence: 99%
“…Barragán‐Montero et al 9 extended this work to develop a more general model that considers variable beam configurations in addition to patient anatomy to predict dose distributions for lung intensity‐modulated radiation therapy (IMRT), thus indicating a potentially easier clinical implementation with no need to train specific models for different beam settings. Similar ideas have been implemented by different research groups and applied to various clinical scenarios 10−13 …”
Section: Introductionmentioning
confidence: 99%