2018
DOI: 10.1002/mp.13262
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A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning

Abstract: Purpose: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy treatment plans. Methods: Eighty cases of early-stage nasopharyngeal cancer (NPC) were included in the study. Seventy cases were chosen randomly as the training set and the remaining as the test set. The inputs were the images with structures, with each target and orga… Show more

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Cited by 151 publications
(177 citation statements)
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“…The recent evolution of deep learning methods has motivated the use of convolutional neural networks (CNN) to predict patient-specific voxel-wise dose distributions from anatomical information (i.e., contours and/or CT), either in a slice-by-slice manner (2D) [39][40][41]43 or directly as a 3D matrix. 38,42,44 The predicted dose distribution can later be used as an objective to automatically generate a treatment plan.…”
Section: Introductionmentioning
confidence: 99%
“…The recent evolution of deep learning methods has motivated the use of convolutional neural networks (CNN) to predict patient-specific voxel-wise dose distributions from anatomical information (i.e., contours and/or CT), either in a slice-by-slice manner (2D) [39][40][41]43 or directly as a 3D matrix. 38,42,44 The predicted dose distribution can later be used as an objective to automatically generate a treatment plan.…”
Section: Introductionmentioning
confidence: 99%
“…The DSC ranged from 0.95 to 1 for different isodose volumes. Chen et al 38 reported for headand-neck cases that the overall mean absolute error for body are 5.5 AE 6.8% and 5.3 AE 6.4% for two types of inputs normalized to prescription. Our results show that the mean absolute 3D dose errors for body is (1.9 AE 1.8)% normalized to prescription.…”
Section: Discussionmentioning
confidence: 99%
“…There are several different types of CNNs, such as LeNet, 33 AlexNet, 24 ZFNet, 34 VGGNet, 35 GoogLeNet, 36 and ResNet, 37 among which ResNet has an excellent performance in ImageNet detection. 37 More recently, Chen et al 38 and Fan et al 39 separately put forward a DL method based on ResNet to predict 3D dose distribution for head-and-neck IMRT patients. Nguyen et al 40 also introduced a U-net 41 model based 3D dose prediction for prostate IMRT patients.…”
Section: Introductionmentioning
confidence: 99%
“…In addition to KBP, DL has been investigated as a mechanism for automated plan generation (72-75). Fan et al (73) and Chen et al (74) independently undertook the similar task to predict dose distribution with a ResNet DL architecture. Although they trained their models on input data of different types and quantities, both groups demonstrated feasible DL-based automated plans that were similar to expert plans.…”
Section: Treatment Planning and Optimizationmentioning
confidence: 99%