A comparative study of deep learning‐based knowledge‐based planning methods for 3D dose distribution prediction of head and neck

Abstract

Purpose

In this paper, we compare four novel knowledge‐based planning (KBP) algorithms using deep learning to predict three‐dimensional (3D) dose distributions of head and neck plans using the same patients’ dataset and quantitative assessment metrics.

Methods

A dataset of 340 oropharyngeal cancer patients treated with intensity‐modulated radiation therapy was used in this study, which represents the AAPM OpenKBP – 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel‐wise dose predictions: U‐Net, attention U‐Net, residual U‐Net (Res U‐Net), and attention Res U‐Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground‐truth using dose statistics and dose‐volume indices.

Results

The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D ₉₉ index for all targets was 0.92 Gy ( p = 0.51) for attention Res U‐Net, 0.94 Gy ( p = 0.40) for Res U‐Net, 2.94 Gy ( p = 0.09) for attention U‐Net, and 3.51 Gy ( p = 0.08) for U‐Net. For the OARs, the values for the $D_{max}$ and $D_{mean}$ indices were 2.72 Gy ( p < 0.01) for attention Res U‐Net, 2.94 Gy ( p < 0.01) for Res U‐Net, 1.10 Gy ( p < 0.01) for attention U‐Net, 0.84 Gy ( p < 0.29) for U‐Net.

Conclusion

All models demonstrated almost comparable performance for voxel‐wise dose prediction. KBP models that employ 3D U‐Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.