The value of automatic segmentation of target volume and organs at risk for nasopharngeal carcinoma / 中华放射肿瘤学杂志

ABSTRACT

Objective:To evaluate the application value of deep deconvolutional neural network (DDNN) model for automatic segmentation of target volume and organs at risk (OARs) in patients with nasopharngeal carcinoma (NPC).Methods:Based on the CT images of 800 NPC patients, an end-to-end automatic segmentation model was established based on DDNN algorithm. Ten newly diagnosed with NPC were allocated into the test set. Using this DDNN model, 10 junior physicians contoured the region of interest (ROI) on 10 patients by using both manual contour (MC) and DDNN deep learning-assisted contour (DLAC) methods independently. The accuracy of ROI contouring was evaluated by using the DICE coefficient and mean distance to agreement (MDTA). The coefficient of variation (CV) and standard distance deviation (SDD) were rendered to measure the inter-observer variability or consistency. The time consumed for each of the two contouring methods was also compared.Results:DICE values of gross target volume (GTV) and clinical target volume (CTV), MDTA of GTV and CTV by using DLAC were 0.67±0.15 and 0.841±0.032, (0.315±0.23) mm and (0.032±0.098) mm, respectively, which were significantly better than those in the MC group (all P<0.001). Except for the spinal cord, lens and mandible, DLAC improved the DICE values of the other OARs, in which mandible had the highest DICE value and optic chiasm had the lowest DICE value. Compared with the MC group, GTV, CTV, CV and SDD of OAR were significantly reduced (all P<0.001), and the total contouring time was significantly shortened by 63.7% in the DLAC group ( P<0.001). Conclusion:Compared with MC, DLAC is a promising method to obtain superior accuracy, consistency, and efficiency for the GTV, CTV and OAR in NPC patients.