Abstract:Background and purpose
Treatment planning of radiotherapy is a time-consuming and planner dependent process that can be automated by dose prediction models. The purpose of this study was to evaluate the performance of two machine learning models for breast cancer radiotherapy before possible clinical implementation.
Materials and methods
An in-house developed model, based on U-net architecture, and a contextual atlas regression forest (cARF) model integrated in the trea… Show more
“…The slice selection was done through Gaussian sampling to select the more important central slices containing the PTVs. The Gaussian scheme had a standard deviation that was equal to one-third of the distance from the central slice to the end slice as previously used by Bakx et al [13] . Figure 1 Visualization of the Euclidean distance map transformation.…”
Section: Methodsmentioning
confidence: 99%
“…A schematic representation of the network is shown in Figure 2 . All model parameters were initially a combination of the parameters used by Bakx et al [13] and the HD U-net from Nguyen et al [16] . Different models with varying parameters were tested to find an optimal working model for our particular patient group [17] , [18] .…”
Section: Methodsmentioning
confidence: 99%
“…All aforementioned studies showed possible solutions for automating the (breast) planning process, but most CNN based studies focus only on the DVH or dose prediction itself without converting the solution into a clinically deliverable plan and performing an evaluation on the clinical acceptability of these plans. During an earlier study from our institution this challenge was tackled by creating a CNN model for the dose prediction of left-sided node negative breast cancer patients and combining it with a dose mimicking algorithm to transform this prediction into a clinically deliverable plan [13] .…”
Background and purpose
Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN).
Materials and methods
Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans.
Results
Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable.
Conclusions
We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.
“…The slice selection was done through Gaussian sampling to select the more important central slices containing the PTVs. The Gaussian scheme had a standard deviation that was equal to one-third of the distance from the central slice to the end slice as previously used by Bakx et al [13] . Figure 1 Visualization of the Euclidean distance map transformation.…”
Section: Methodsmentioning
confidence: 99%
“…A schematic representation of the network is shown in Figure 2 . All model parameters were initially a combination of the parameters used by Bakx et al [13] and the HD U-net from Nguyen et al [16] . Different models with varying parameters were tested to find an optimal working model for our particular patient group [17] , [18] .…”
Section: Methodsmentioning
confidence: 99%
“…All aforementioned studies showed possible solutions for automating the (breast) planning process, but most CNN based studies focus only on the DVH or dose prediction itself without converting the solution into a clinically deliverable plan and performing an evaluation on the clinical acceptability of these plans. During an earlier study from our institution this challenge was tackled by creating a CNN model for the dose prediction of left-sided node negative breast cancer patients and combining it with a dose mimicking algorithm to transform this prediction into a clinically deliverable plan [13] .…”
Background and purpose
Treatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN).
Materials and methods
Data of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans.
Results
Dose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable.
Conclusions
We created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.
“…The second model was developed by RaySearch and is based on contextual atlas regression forests (cARF)[ 10 ]. A more detailed description of both models and their training on in-house clinical data was previously published by Bakx et al [ 7 ] and can also be found in the Additional file 1 . The current study focused on the clinical applicability of both models.…”
Background
Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists.
Methods
For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time.
Results
The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model.
Conclusions
Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.
“…The dose prediction model can make an end-to-end mapping transformation between patients' anatomical and dose distribution information with organs-atrisk (OARs) constraints (9)(10)(11)(12). Compared with using the conventional treatment planning system (TPS), using the DL model to generate predicted dose distribution reduces planning time significantly (13)(14)(15)(16).…”
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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