Feasibility of automated planning for whole-brain radiation therapy using deep learning

The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.

A dataset of 55 cases, including 40 training sets, five validation sets, and 10 test sets, was used to predict the static MLC shape. The digitally reconstructed radiograph (DRR) reconstructed from planning CT images as an input layer and the MLC shape as an output layer are connected one-to-one via the U-net modeling. The Dice similarity coefficient (DSC) was used as the loss function in the training and ninefold cross-validation. Dose-volume-histogram (DVH) curves were constructed for assessing the automatic MLC shaping performance. Deep-learning (DL) and manually optimized (MO) approaches were compared based on the DVH curves and dose distributions.

The ninefold cross-validation ensemble test results were consistent with DSC values of 94.6 ± 0.4 and 94.7 ± 0.9 in training and validation learnings, respectively. The dose coverages of 95% target volume were (98.0 ± 0.7)% and (98.3 ± 0.8)%, and the maximum doses for the lens as critical organ-at-risk were 2.9 Gy and 3.9 Gy for DL and MO, respectively. The DL technique shows the consistent results in terms of the DVH parameter except for MLC shaping prediction for dose saving of small organs such as lens.

Comparable with the MO plan result, the WBRT plan quality obtained using the DL approach is clinically acceptable. Moreover, the DL approach enables WBRT auto-planning without the time-consuming manual MLC shaping and target contouring.