Convolutional neural network-based dosimetry evaluation of esophageal radiation treatment planning

A dosimetry evaluation model for treatment planning of esophageal radiation therapy is developed using a deep learning model. The model predicts dose volume histogram (DVH) from distance to target histogram (DTH) based on stacked de-noise auto-encoder (SDAE) and one-dimensional convolutional network (1D-CN).

First, SDAE is used to extract the features from the curves of DTH and DVH. Then 1D-CN model is employed to learn the relationship between the features of DTH and DVH, and later used to predict the features of DVH from the features of DTH. Finally, the curve of DVH is restored from the features of DVH based on SDAE. Two hundred and seventy treatment plans are used for training 1D-CN and another sixty-three treatment plans are used for evaluating this model. This method is also compared with another two popular prediction methods based on support vector machine (SVM) and U-net.

Based on the experimental result, the proposed model achieves the lowest dose endpoint error comparing to the other models. The average prediction error on planned target volume, left lung, right lung, heart, and spinal cord is 2.94% for the proposed model, while the average prediction errors are 6.79% and 3.41% for SVM and U-net, respectively.

A dosimetry evaluation method based on SDAE and 1D-CN is developed in characterizing the correlation relationship between DTH and DVH of treatment plans. The results show that the model could be trained more efficiently in this framework and the DVH could be predicted with higher accuracy comparing to those existing methods. It provides a useful tool in supporting automated treatment planning of esophageal intensity-modulated radiotherapy.