For 20 cases, deep-learning delineation outperformed 4 of the 8 radiation oncologists, with mean DSC of 0.766 against 0.691, 0.699, 0.704 and 0.719 (all P < 0.05); while performed comparably to another 4 oncologists. With the assistance of deep-learning delineation, increased delineation accuracy was observed in 5 oncologists (mean DSC increased from 0.731 to 0.779; all P < 0.05) and stable DSC in 3 oncologists. Furthermore, decreased multi-observer DSC was observed (0.774 AE 0.0773 vs. 0.702 AE 0.114; P < 0.001). Average time spent decreased from 30.2 min on manual delineation to 18.3 min in editing deep-learning delineation (P < 0.001), saving 39.2% of the time. Conclusion: In GTV p delineation for NPC, deep-learning delineation achieved satisfactory agreement in comparison with expert-panel delineation, and outperformed 4 of 8 qualified radiation oncologists significantly. Assistance of deep-learning delineation consistently improved both the accuracy and efficiency.
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