3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification

Zhang, Binghao; Babier, Aaron; Chan, Timothy C. Y.; Ruschin, Mark

doi:10.1016/j.ejmp.2023.102533

Cited by 3 publications

(9 citation statements)

References 29 publications

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“…Our dose predictions exhibited consistently low dose differences and high gamma passing rates, comparable to what have been previously observed. 14 These results indicate the similarity between the predictions and clinical plans. However, our observations suggest that the dose predictions do not necessarily have to resemble clinical plans on a per-voxel basis to yield satisfactory KBP plans.…”

Section: Discussionmentioning

confidence: 52%

“…Additionally, the pipeline can create plans for a heterogeneous patient population as the prediction model was previously shown to be effective for a large range of target shapes and sizes. 14 Furthermore, the pipeline offers flexibility to incorporate practices specific to each center by utilizing a prediction model trained on past cases treated at a particular center, or by integrating treatment objectives unique to each center in the optimization model. This allows for the system to be easily implemented across multiple centers, particularly for centers which are just starting up, and act as a generalizable tool to bring into countries without existing GK infrastructure.…”

Section: Discussionmentioning

confidence: 99%

“…The patient data underwent several pre-processing steps to facilitate the dose prediction, as described previously in literature. 14 To train the deep learning model, the target contours for each of the 322 patients in the training dataset was first transformed into masks, which were then isolated and cropped, with targets in close proximity taken together. The MRI images and clinical doses were cropped in the same regions.…”

Section: Dose Predictionsmentioning

confidence: 99%

“…In existing KBP literature, most research has focused on the first stage -a dose prediction model -that use machine learning methods to generate dose predictions. [6][7][8][9][10][11][12][13][14] Translating these predictions into plans requires a second stage -a plan optimization model -that uses either dose mimicking (DM) 15 or inverse planning 16 to generate a treatment plan. In DM, a plan with as similar a dose distribution to the prediction is generated based on linear or quadratic dose differences.…”

Section: Introductionmentioning

confidence: 99%

“…In existing KBP literature, most research has focused on the first stage – a dose prediction model – that use machine learning methods to generate dose predictions 6–14 . Translating these predictions into plans requires a second stage – a plan optimization model – that uses either dose mimicking (DM) 15 or inverse planning 16 to generate a treatment plan.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based planning for Gamma Knife

Zhang,

Babier,

Ruschin

et al. 2024

Medical Physics

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BackgroundCurrent methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge‐based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created for GK.PurposeTo develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK.MethodsData were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out‐of‐sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dose predictions.ResultsAcross all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 0.158 and 3.356 1.030 respectively, compared to 0.713 0.124 and 3.452 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans.ConclusionsPlans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal human intervention.

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Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Dose Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based planning for Gamma Knife

Zhang,

Babier,

Ruschin

et al. 2024

Medical Physics

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Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases

Pan,

Xiao,

Ruan

et al. 2023

Front. Oncol.

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PurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.MethodsWe collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.ResultsDose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).ConclusionThis study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.

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Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy

Wang,

Pan,

Zhang

et al. 2024

Front. Oncol.

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PurposeThe 3D U-Net deep neural network structure is widely employed for dose prediction in radiotherapy. However, the attention to the network depth and its impact on the accuracy and robustness of dose prediction remains inadequate.Methods92 cervical cancer patients who underwent Volumetric Modulated Arc Therapy (VMAT) are geometrically augmented to investigate the effects of network depth on dose prediction by training and testing three different 3D U-Net structures with depths of 3, 4, and 5.ResultsFor planning target volume (PTV), the differences between predicted and true values of D98, D99, and Homogeneity were statistically 1.00 ± 0.23, 0.32 ± 0.72, and -0.02 ± 0.02 for the model with a depth of 5, respectively. Compared to the other two models, these parameters were also better. For most of the organs at risk, the mean and maximum differences between the predicted values and the true values for the model with a depth of 5 were better than for the other two models.ConclusionsThe results reveal that the network model with a depth of 5 exhibits superior performance, albeit at the expense of the longest training time and maximum computational memory in the three models. A small server with two NVIDIA GeForce RTX 3090 GPUs with 24 G of memory was employed for this training. For the 3D U-Net model with a depth of more than 5 cannot be supported due to insufficient training memory, the 3D U-Net neural network with a depth of 5 is the commonly used and optimal choice for small servers.

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3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification

Cited by 3 publications

References 29 publications

Knowledge‐based planning for Gamma Knife

Knowledge‐based planning for Gamma Knife

Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases

Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy

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