A feasibility study on deep learning-based individualized 3D dose distribution prediction

Abstract

Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs.

Methods: In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients.

Results: The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose.

Conclusions: In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.

Keywords: Pareto optimal dose distribution prediction; deep learning; dose volume histogram; physicians’ preferred trade-offs.

Figure 1.

The envisioned clinical workflow based on the proposed DL model.

Figure 2.

The workflow of the proposed method for model training and testing.

Figure 3.

Modified 3D U-Net architecture with two inputs and one output. The green boxes denote multi-channel 3D feature maps, and each white box indicates a copied 3D feature map. The number at the top of the box represents the channel number for each feature map, and the map size is at the lower left corner of the box.

Figure 4.

Loss values as a function of epochs in the training phase. Although the validation loss (orange line) has some oscillations, both the training loss (blue line) and the validation loss follow a convergence trend.

Figure 5.

Individualized dose distributions predicted for a testing patient. From left column to right column: contours, true dose, predicted dose distribution, difference map (true – prediction), and DVH comparison between true (solid line) and predicted (dashed line) dose distributions. The input desired DVH curves are also shown in dotted lines. All dose values are normalized to the prescription dose. Each row represents a Pareto optimal plan of a different PTV/OAR trade-off for this patient.

Figure 6.

Violin plot of the differences, normalized to the prescription dose, between the predicted and true (a) mean and (b) maximum doses in each OAR and PTV for all test patients.