Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) and organs at risk (OARs). DVH is highly correlated with the geometrical relationship between PTV and OARs, which motivates us to explore deep learning techniques to model such correlation and predict DVHs of different OARs. Distance to target histogram (DTH) is chosen to measure the geometrical relationship between PTV and OARs. DTH and DVH features are then undergone dimension reduction by autoencoder. The reduced feature vectors are finally imported into deep belief network to model the correlation between DTH and DVH. This correlation can be used to predict DVH of the corresponding OAR for new patients. Validation results revealed that the relative dose difference of the predicted and clinical DVHs on four different OARs were less than 3%. These promising results suggested that the predicted DVH could provide near-optimal parameters to significantly reduce the planning time.