ABSTRACT
Objective:
To explore a three-dimensional
dose distribution prediction
method for the left
breast cancer radiotherapy planning based on full convolution network (FCN), and to evaluate the accuracy of the prediction model.
Methods:
FCN was utilized to achieve three-dimensional
dose distribution prediction. First, a
volumetric modulated arc therapy (VMAT) plan
dataset with 60 cases of left
breast cancer was built. Ten cases were randomly chosen from the
dataset as the test set, and the remaining 50 cases were used as the
training set. Then, a U-Net model was built with the organ structure matrix as inputs and
dose distribution matrix as outputs. Finally, the model was adopted to predict the
dose distribution of the cases in the test set, and the predicted 3D doses were compared with actual planned results.
Results:
The mean absolute differences of PTV, ipsilateral
lung,
heart, whole
lung and
spinal cord for 10 cases were (119.95±9.04) cGy, (214.02±9.04) cGy, (116.23±30.96) cGy, (127.67±69.19) cGy, and (37.28±18.66) cGy, respectively. The Dice similarity coefficient (DSC) of the prediction
dose and the planned
dose in the 80% and 100%
prescription dose range were 0.92±0.01 and 0.92±0.01. The γ rate of 3 mm/3% in the area of 80% and 10%
prescription dose range were 0.85±0.03 and 0.84±0.02.
Conclusion:
FCN can be used to predict the three-dimensional
dose distribution of left
breast cancer patients undergoing VMAT.