Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies

Yang, Zhong; Yu, Long-Chuan; Zhao, Jun; Fang, Yun; Yong, Yang; Wu, Zhiqiang; Wang, Jiazhou; Hu, Weigang

doi:10.3389/fonc.2021.697995

Cited by 9 publications

(12 citation statements)

References 35 publications

(28 reference statements)

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“…Handcrafted features on the patient plan cannot cover all inherent structure characteristics and only capture low‐level features, so the model will not be sufficiently accurate for dose prediction 21 . On the other hand, contemporary KBP algorithms, which use the state‐of‐the‐art deep learning techniques, predict more accurate and robust full three‐dimensional (3D) dose distributions (which are used to generate post‐optimization deliverable plans) 22–27 . DVH can be fully reconstructed from the predicted 3D dose distributions, and the dose constraints can then be calculated.…”

Section: Introductionmentioning

confidence: 99%

“…21 On the other hand, contemporary KBP algorithms, which use the state-of -theart deep learning techniques, predict more accurate and robust full three-dimensional (3D) dose distributions (which are used to generate post-optimization deliverable plans). [22][23][24][25][26][27] DVH can be fully reconstructed from the predicted 3D dose distributions, and the dose constraints can then be calculated. Generating deliverable treatment plans involves calculations of the multileaf collimator leaf motion sequence to ensure that the predicted dose distributions satisfy the physical delivery constraints imposed by the linac.…”

Section: Introductionmentioning

confidence: 99%

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Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

Osman

2022

J Applied Clin Med Phys

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Purpose Deep learning–based knowledge‐based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high‐quality plans. This paper presents a novel KBP model using an attention‐gating mechanism and a three‐dimensional (3D) U‐Net for intensity‐modulated radiation therapy (IMRT) 3D dose distribution prediction in head‐and‐neck cancer. Methods A total of 340 head‐and‐neck cancer plans, representing the OpenKBP—2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention‐gated 3D U‐Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean‐squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U‐Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning‐based dose prediction models. Results The proposed attention‐gated 3D U‐Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground‐truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U‐Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U‐Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U‐Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U‐Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U‐Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U‐Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U‐Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U‐Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U‐Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the Dmax${D_{max}}$ (brainstem, spinal cord, and mandible) and Dmean${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground‐truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground‐truth plans. The proposed model needs ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

Osman

2022

J Applied Clin Med Phys

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“…They mentioned that dose distribution could be predicted utilizing a fluence map as well. Furthermore, this enlightens us to get the dose prediction based on an auto-planning system ( 27 , 28 ). Dose prediction studies can be the basis for much RT-relevant research and technology development.…”

Section: Discussionmentioning

confidence: 99%

Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy

Liu

Chen²,

Wang

et al. 2021

Front. Oncol.

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PurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the “3D Dense-U-Net”, which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis.ResultsWe found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one.ConclusionsIt is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.

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“…Based on the clinical post-optimization strategies, Zhong et al. ( 9 ) designed a new automatic radiotherapy planning strategy that was able to produce clinically acceptable dose distributions.…”

Section: Introductionmentioning

confidence: 99%

“…To take full advantage of the historical patient data, Mardani et al (8) proposed a learning empowered approach which employed a multi-task linear regression model to predict 3D dose volume for a new patient by extracting the shared features of historical patients and their tumor shapes. Based on the clinical post-optimization strategies, Zhong et al (9) designed a new automatic radiotherapy planning strategy that was able to produce clinically acceptable dose distributions.…”

Section: Introductionmentioning

confidence: 99%

CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network

et al. 2022

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PurposeCurrent deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically.Materials and MethodsWe developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients.ResultsExperimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas).ConclusionThe proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT

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Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies

Cited by 9 publications

References 35 publications

Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

Attention‐aware 3D U‐Net convolutional neural network for knowledge‐based planning 3D dose distribution prediction of head‐and‐neck cancer

Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy

CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network

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