Auditing the clinical usage of deep-learning based organ-at-risk auto-segmentation in radiotherapy - Josh Mason; Jack Doherty; Sarah Robinson; Meagan de la Bastide; Jack Miskell; Ruth McLauchlan (Physics and Imaging in Radiation Oncology)
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.