A comparative study of deep learning‐based knowledge‐based planning methods for 3D dose distribution prediction of head and neck

Osman, Alexander F. I.; Tamam, Nissren; Yousif, Yousif

doi:10.1002/acm2.14015

Cited by 8 publications

(4 citation statements)

References 42 publications

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“… 12 , 25 , 26 As a kind of typical transformers-based DL model, UNETR has never been used in dose prediction. 27 Research from Osman et al 28 showed that U-Net, attention U-Net, residual U-Net, and attention Res U-Net for H&N plans from OpenKBP-Grand Challenge data 7 had an almost comparable performance for voxel-wise dose prediction. Inspired by Osman et al's research, 28 we compared the 3D U-Net and its typical variants.…”

Section: Discussionmentioning

confidence: 99%

“… 27 Research from Osman et al 28 showed that U-Net, attention U-Net, residual U-Net, and attention Res U-Net for H&N plans from OpenKBP-Grand Challenge data 7 had an almost comparable performance for voxel-wise dose prediction. Inspired by Osman et al's research, 28 we compared the 3D U-Net and its typical variants. Our results show that the 4 DL models have high capability in accurately predicting 3D dose distributions for cervical cancer VMAT plans.…”

Section: Discussionmentioning

confidence: 99%

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A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy

Wu,

Liu,

Pang

et al. 2024

Technol Cancer Res Treat

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Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients’ plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy

Wu,

Liu,

Pang

et al. 2024

Technol Cancer Res Treat

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“…In addition, other types of networks, such as Resnet ( 27 , 29 , 30 ) and GAN ( 31 – 33 ), are also used for dose prediction. So far, the deep U-net-like architecture and its variants with various types of residual or dense blocks become the mainstream structure for dose prediction ( 34 – 38 ).…”

Section: Introductionmentioning

confidence: 99%

“…With the successful applications of deep learning models in predicting dose distribution for many primary tumor sites such as the lung ( 25 , 26 ), head-and-neck ( 23 , 28 , 33 , 34 ), and prostate ( 21 , 35 ), it is interesting to investigate this application for brain metastasis. In the study, a deep U-net architecture ( 30 ), previously successfully applied to predict dose distribution for head-and-neck cancer patients, is used as the base model in predicting the dose distribution of the VMAT plan for brain metastasis.…”

Section: Introductionmentioning

confidence: 99%

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases

Huang,

Shang,

et al. 2024

Front. Oncol.

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PurposeBrain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan. In this study, a deep learning model is applied to the dose prediction of a single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases.MethodA U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. The 3D dose distribution is then predicted with the input of the CT image and contours of regions of interest (ROIs). A total of 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs), including (Dmax and Dmean) for OARs, (D98, D95, D50, and D2) for PTVs, homogeneity index, and conformity index. The similarity between the predicted and clinically approved plan dose distribution is also evaluated.ResultFor 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%. The mean absolute differences of D98, D95, D50, and D2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0%, and 3.0%, respectively. The prediction accuracy of OARs for Dmax and Dmean is within 3.2% and 1.2%, respectively. The average DSC ranges from 0.86 to 1 for all tested patients.ConclusionU-ResNet is viable to produce accurate dose distribution that is comparable to those of the clinically approved treatment plans. The predicted results can be used to improve current treatment planning design, plan quality, efficiency, etc.

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Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi‐scale dilated network approach with scale‐balanced structure loss

Huang,

Gao,

et al. 2024

J Applied Clin Med Phys

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PurposePartial stereotactic ablative boost radiotherapy (P‐SABR) effectively treats bulky lung cancer; however, the planning process for P‐SABR requires repeated dose calculations. To improve planning efficiency, we proposed a novel deep learning method that utilizes limited data to accurately predict the three‐dimensional (3D) dose distribution of the P‐SABR plan for bulky lung cancer.MethodsWe utilized data on 74 patients diagnosed with bulky lung cancer who received P‐SABR treatment. The patient dataset was randomly divided into a training set (51 plans) with augmentation, validation set (7 plans), and testing set (16 plans). We devised a 3D multi‐scale dilated network (MD‐Net) and integrated a scale‐balanced structure loss into the loss function. A comparative analysis with a classical network and other advanced networks with multi‐scale analysis capabilities and other loss functions was conducted based on the dose distributions in terms of the axial view, average dose scores (ADSs), and average absolute differences of dosimetric indices (AADDIs). Finally, we analyzed the predicted dosimetric indices against the ground‐truth values and compared the predicted dose‐volume histogram (DVH) with the ground‐truth DVH.ResultsOur proposed dose prediction method for P‐SABR plans for bulky lung cancer demonstrated strong performance, exhibiting a significant improvement in predicting multiple indicators of regions of interest (ROIs), particularly the gross target volume (GTV). Our network demonstrated increased accuracy in most dosimetric indices and dose scores in different ROIs. The proposed loss function significantly enhanced the predictive performance of the dosimetric indices. The predicted dosimetric indices and DVHs were equivalent to the ground‐truth values.ConclusionOur study presents an effective model based on limited datasets, and it exhibits high accuracy in the dose prediction of P‐SABR plans for bulky lung cancer. This method has potential as an automated tool for P‐SABR planning and can help optimize treatments and improve planning efficiency.

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A comparative study of deep learning‐based knowledge‐based planning methods for 3D dose distribution prediction of head and neck

Cited by 8 publications

References 42 publications

A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy

A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases

Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi‐scale dilated network approach with scale‐balanced structure loss

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