ESTRO 2020 Abstract book

S373 ESTRO 2020

Results Outlier scoring identified 11% and 6 % of sCT and AC test slices, respectively, as lying outside the training data confidence bound, indicating a risk of DL prediction failure. Failures originated from variation in scan extent, MRI signal or artefacts. For inlier slices, local confidence maps (fig. 2) were well correlated to local HU differences (r=0.83) for sCT and with confusion entropy in multiclass segmentation (r=0.97). Cohort mean MAE for sCT was 65 (s.d. 9.3) HU and mean DD95 PTV was 0.8% (s.d. 0.5). Outlier slices detected by 1-SVM exhibited significantly larger HU differences (p<0.001) and were often visually unacceptable.

Conclusion AutoConfidence can identify data outliers and low- confidence prediction regions of DL predictions, independent of the production network, enabling automated per-patient validation of 'black box' methods. Regions requiring human intervention can be highlighted for review, increasing clinical confidence and facilitating highly efficient automated workflows for (e.g.) online adaptive re-planning. PH-0607 Investigating the potential of deep learning for quality assurance of organ-at-risk contours H. Nijhuis 1 , W. Van Rooij 1 , V. Gregoire 2 , J. Overgaard 3 , B. Slotman 1 , W. Verbakel 1 , M. Dahele 1 1 Amsterdam UMC, Radiotherapy, Amsterdam, The Netherlands ; 2 Université Catholique de Louvain, Radiation Oncology, Brussels, Belgium ; 3 Aarhus University Hospital, Department of Experimental Clinical Oncology, Aarhus, Denmark Purpose or Objective Quality assurance (QA) of radiotherapy contours is time and labor intensive and subject to inter-observer- variability. Automated QA could be far more efficient. We investigated whether deep-learning (DL) is a feasible and