Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

Song, Ying; Hu, Junjie; Liu, Yang; Hu, Haofeng; Huang, Yang; Bai, Song; Zhang, Yi

doi:10.1016/j.radonc.2020.05.005

Cited by 47 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with their prediction results 9 , the difference between the deep neural network method and the IDVHs method at D 98 of PTV 50 was (ε, 0.24% vs 0.28%). The predicted PTV dosimetric results of this study are comparable to the results of Fan et al 8 and Song et al 9 .…”

Section: Ptv Dvh Prediction Accuracy Of the Idvhs Method The Comparisupporting

confidence: 88%

“…The patient geometric information-based three-dimensional (3D) dose prediction model has been widely studied and reported in recent years and can provide the predicted dosimetric results for OAR and PTV [6][7][8][9] . Fan et al applied a deep learning-based model to predict the 3D dose distribution for head-and-neck cancer and no…”

Section: Discussionmentioning

confidence: 99%

“…With the continuous development of artificial intelligence and machine learning technology, a medical computerized clinical decision support and assistance systems based on more available clinical data have played an increasingly important role in helping clinicians make clinical decisions 1,2 . In the field of radiotherapy, making dose-volume histogram (DVH) or dose distribution of organ at risk (OAR) predictions based on prior plan data could provide a valuable dose-volume reference that could help planners determine whether the quality of a treatment plan could be further improved [3][4][5][6][7][8][9][10][11] and could be used as the dose-volume optimization input constraints in a treatment planning system (TPS) to assist in plan generation [12][13][14][15][16][17] . In addition, a machine learning method could predict the dose-volume parameter such as dose distribution index for treatment plan evaluation which is helpful for fast plan quality evaluation 18 .…”

mentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

Jiao

Wang

Chen

et al. 2021

Sci Rep

View full text Add to dashboard Cite

The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R2 and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D98, D95, D1 and uniformity index (UI). The OAR dose was compared using the maximum dose, V30 and V40. The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D98,D95,D1 and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R2, MAE, the maximum dose, V30 and V40 (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.

show abstract

Section: Ptv Dvh Prediction Accuracy Of the Idvhs Method The Comparisupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

Jiao

Wang

Chen

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…He et al reformulated the layers as a learning residual function instead of directly fitting a desired underlying mapping. Chen et al and Fan et al proposed the DL method based on ResNet with 101 and 50 weight layers, respectively, to predict dose distribution for head/neck cancer IMRT patients 133,143 . Since networks with very deep layers are difficult to train due to vanishing gradient, such networks used shortcut connections to add to the outputs of the stacked layers 170 .…”

Section: Resultsmentioning

confidence: 99%

Knowledge‐based radiation treatment planning: A data‐driven method survey

Momin

Lei

et al. 2021

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…Deep learning methods based on dose prediction of IMRT plans have been utilized for head and neck [22,23], rectal [24], prostate [25,26], and lung [27] cancer cases. In addition, dose prediction using VMAT plans has been performed for head and neck [28], rectal [29], and prostate [30] cancer. Other treatment techniques, such as helical tomotherapy [31] and 3D dose prediction, have been employed for head and neck cancer.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Method for Prediction of Patient-Specific Dose Distribution in Breast Cancer

Ahn

Kim²,

Kim³

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundPatient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning and its performance was compared with that of conventional knowledge-based planning using RapidPlan™.MethodsPatient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction.ResultsThe mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ±0.82%, 0.52 ± 0.97% -0.88 ± 1.83%, -1.16 ± 2.58%, and -0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.04%, 0.87 ± 0.63%, -0.29 ± 0.98%, 1.30 ± 0.86%, -0.32 ± 1.10%, 0.12 ± 2.13, and -1.74 ±1.79%, respectively.ConclusionsIn this study, a deep learning method for dose prediction was developed and was demonstrated to predict patient-specific doses for left-sided breast cancer accurately. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan. The proposed model will be able to maintain treatment plan quality and increase efficiency through patient dose prediction.

show abstract

Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

Cited by 47 publications

References 21 publications

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

Knowledge‐based radiation treatment planning: A data‐driven method survey

Deep Learning Method for Prediction of Patient-Specific Dose Distribution in Breast Cancer

Contact Info

Product

Resources

About