Educative Impact of Automatic Delineation Applied to Head and Neck Cancer Patients on Radiation Oncology Residents.

Abstract

To evaluate the educational impact on radiation oncology residents in training when introducing an automatic segmentation software in head and neck cancer patients regarding organs at risk (OARs) and prophylactic cervical lymph node level (LNL) volumes. Two cases treated by exclusive intensity-modulated radiotherapy were delineated by an expert radiation oncologist and were considered as reference. Then, these cases were delineated by residents divided into two groups: group 1 (control group), experienced residents delineating manually, group 2 (experimental group), young residents on their first rotation trained with automatic delineation, delineating manually first (M -) and then after using the automatic system (M +). The delineation accuracy was assessed using the Overlap Volume (OV). Regarding the OARs, mean OV was 0.62 (SD = 0.05) for group 1, 0.56 (SD = 0.04) for group 2M - , and 0.61 (SD = 0.03) for group 2M + . Mean OV was higher in group 1 compared to group 2M - (p = 0.01). There was no OV difference between group 1 and group 2M + (p = 0.67). Mean OV was higher in the group 2M + compared to group 2M - (p < 0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2M - , and 0.58 (SD = 0.04) in group 2M + . Mean OV was higher in group 2M + for 11 of the 12 analysed structures compared to group 2M - (p = 0.016). Prior use of the automatic delineation software reduced the average contouring time per case by 34 to 40%. Prior use of atlas-based automatic segmentation reduces the delineation duration, and provides reliable OARs and LNL delineations.