Sarrade Thomas, Gautier Michael, Schernberg Antoine, Jenny Catherine, Orthuon Alexandre, Maingon Philippe, Huguet Florence
Department of Radiation Oncology, Tenon Hospital, AP-HP.Sorbonne Université, 4 rue de la Chine, 75020, Paris, France.
Department of Medical Physics, AP-HP.Sorbonne Université, Paris, France.
J Cancer Educ. 2023 Apr;38(2):578-589. doi: 10.1007/s13187-022-02157-9. Epub 2022 Apr 1.
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 2 M - , and 0.61 (SD = 0.03) for group 2 M + . Mean OV was higher in group 1 compared to group 2 M - (p = 0.01). There was no OV difference between group 1 and group 2 M + (p = 0.67). Mean OV was higher in the group 2 M + compared to group 2 M - (p < 0.003). Regarding LNL, mean OV was 0.53 (SD = 0.06) in group 1, 0.54 (SD = 0.03) in group 2 M - , and 0.58 (SD = 0.04) in group 2 M + . Mean OV was higher in group 2 M + for 11 of the 12 analysed structures compared to group 2 M - (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.
为评估在头颈部癌患者中引入自动分割软件对放射肿瘤学住院医师培训的教育影响，该软件用于勾画危及器官（OARs）和预防性颈部淋巴结水平（LNL）的体积。由一位放射肿瘤学专家勾画了两例仅接受调强放疗的病例，并将其作为参考。然后，将住院医师分为两组对这些病例进行勾画：第1组（对照组），经验丰富的住院医师手动勾画；第2组（实验组），首次轮转的年轻住院医师，先手动勾画（M -），然后在使用自动系统后再进行勾画（M +）。使用重叠体积（OV）评估勾画准确性。对于OARs，第1组的平均OV为0.62（标准差=0.05），第2组M -为0.56（标准差=0.04），第2组M +为0.61（标准差=0.03）。第1组的平均OV高于第2组M -（p = 0.01）。第1组和第2组M +之间的OV无差异（p = 0.67）。第2组M +的平均OV高于第2组M -（p < 0.003）。对于LNL，第1组的平均OV为0.53（标准差=0.06），第2组M -为0.54（标准差=0.03），第2组M +为0.58（标准差=0.04）。与第2组M -相比，在分析的12个结构中的11个结构中，第2组M +的平均OV更高（p = 0.016）。预先使用自动勾画软件可将每个病例的平均轮廓勾画时间减少34%至40%。预先使用基于图谱的自动分割可减少勾画时间，并提供可靠的OARs和LNL勾画。
