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 5 publications

(4 citation statements)

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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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Attention 3D U‐NET for dose distribution prediction of high‐dose‐rate brachytherapy of cervical cancer: Direction modulated brachytherapy tandem applicator

Gautam,

Osman,

Richeson

et al. 2024

Medical Physics

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BackgroundDirection Modulated Brachytherapy (DMBT) enables conformal dose distributions. However, clinicians may face challenges in creating viable treatment plans within a fast‐paced clinical setting, especially for a novel technology like DMBT, where cumulative clinical experience is limited. Deep learning‐based dose prediction methods have emerged as effective tools for enhancing efficiency.PurposeTo develop a voxel‐wise dose prediction model using an attention‐gating mechanism and a 3D UNET for cervical cancer high‐dose‐rate (HDR) brachytherapy treatment planning with DMBT six‐groove tandems with ovoids or ring applicators.MethodsA multi‐institutional cohort of 122 retrospective clinical HDR brachytherapy plans treated to a prescription dose in the range of 4.8–7.0 Gy/fraction was used. A DMBT tandem model was constructed and incorporated onto a research version of BrachyVision Treatment Planning System (BV‐TPS) as a 3D solid model applicator and retrospectively re‐planned all cases by seasoned experts. Those plans were randomly divided into 64:16:20 as training, validating, and testing cohorts, respectively. Data augmentation was applied to the training and validation sets to increase the size by a factor of 4. An attention‐gated 3D UNET architecture model was developed to predict full 3D dose distributions based on high‐risk clinical target volume (CTVHR) and organs at risk (OARs) contour information. The model was trained using the mean absolute error loss function, Adam optimization algorithm, a learning rate of 0.001, 250 epochs, and a batch size of eight. In addition, a baseline UNET model was trained similarly for comparison. The model performance was evaluated on the testing dataset by analyzing the outcomes in terms of mean dose values and derived dose‐volume‐histogram indices from 3D dose distributions and comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinically meaningful dosimetric indices.ResultsThe proposed attention‐gated 3D UNET model showed competitive accuracy in predicting 3D dose distributions that closely resemble the ground‐truth dose distributions. The average values of the mean absolute errors were 1.82 ± 29.09 Gy (vs. 6.41 ± 20.16 Gy for a baseline UNET) in CTVHR, 0.89 ± 1.25 Gy (vs. 0.94 ± 3.96 Gy for a baseline UNET) in the bladder, 0.33 ± 0.67 Gy (vs. 0.53 ± 1.66 Gy for a baseline UNET) in the rectum, and 0.55 ± 1.57 Gy (vs. 0.76 ± 2.89 Gy for a baseline UNET) in the sigmoid. The results showed that the mean absolute error (MAE) for the bladder, rectum, and sigmoid were 0.22 ± 1.22 Gy (3.62%) (p = 0.015), 0.21 ± 1.06 Gy (2.20%) (p = 0.172), and ‐0.03 ± 0.54 Gy (1.13%) (p = 0.774), respectively. The MAE for D90, V100%, and V150% of the CTVHR were 0.46 ± 2.44 Gy (8.14%) (p = 0.018), 0.57 ± 11.25% (5.23%) (p = 0.283), and ‐0.43 ± 19.36% (4.62%) (p = 0.190), respectively. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for any new patient plan, thus making it sufficient for near real‐time applications and aiding with decision‐making in the clinic.ConclusionsAttention gated 3D‐UNET model demonstrated a capability in predicting voxel‐wise dose prediction, in comparison to 3D UNET, for DMBT intracavitary brachytherapy planning. The proposed model could be used to obtain dose distributions for near real‐time decision‐making before DMBT planning and quality assurance. This will guide future automated planning, making the workflow more efficient and clinically viable.

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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 5 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

Attention 3D U‐NET for dose distribution prediction of high‐dose‐rate brachytherapy of cervical cancer: Direction modulated brachytherapy tandem applicator

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