Mason Josh, Doherty Jack, Robinson Sarah, de la Bastide Meagan, Miskell Jack, McLauchlan Ruth
Department of Radiobiology and Radiation Physics, Imperial College Healthcare NHS Trust, Charing Cross Hospital, Fulham Palace Road W6 8RF London, UK.
Phys Imaging Radiat Oncol. 2025 Jan 30;33:100716. doi: 10.1016/j.phro.2025.100716. eCollection 2025 Jan.
For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. The audit identified changes in editing corresponding to vendor model changes, adaption of local contouring practice and reduced editing in areas of no clinical significance. The audit allowed assessment of the level and frequency of editing and identification of outlier cases.
在深度学习自动分割（DLAS）临床应用后的18个月里，对危及器官（OAR）轮廓编辑进行了一次审核，涵盖了来自单一机构的1255名患者以及大多数肿瘤部位。平均表面骰子相似系数从0.87提高到0.97，未编辑的OAR数量从21.5%增加到40%。审核发现编辑方面的变化与供应商模型的变化、局部轮廓绘制实践的调整以及临床无意义区域编辑的减少相对应。该审核有助于评估编辑的水平和频率，并识别异常病例。
