The impact of data consistency on deep learning models for nasopharyngeal cancer organ auto-segmentation.

Purpose. To investigate how annotation consistency influences deep learning-based auto-contouring performance for organs-at-risk (OARs) in nasopharyngeal cancer radiotherapy.Methods. We evaluated CT scans from 1,301 nasopharyngeal carcinoma patients: 65 contoured by Physician A, 76 by Physician B, and 1,160 by heterogeneous multi-physician teams. Three cohorts (50 samples each for Physicians A/B; 1,000 for multi-physician) with standardized U-Net training protocols generated Models A, B, and C. Model C underwent physician-specific fine-tuning. Performance was quantified via Dice similarity coefficients (DSC) against ground-truth contours across 14 critical OARs.Results. Each model achieved peak accuracy on physician-matched test data. Critically, small-consistency models (A/B) outperformed large-heterogeneous Model C on target cohorts (Model A: 0.777 versus Model C's 0.743 on Test A; Model B: 0.806 versus 0.765 on Test B). Physician-specific fine-tuning closed institutional data gaps, boosting Model C's DSC to 0.795 (+7.08% versus original) on Test A and 0.814 (+6.32%) on Test B-surpassing both original Model C and dedicated small-data models (Model A: +2.39%; Model B: +0.97%).Conclusion. Annotation consistency supersedes dataset scale as the primary performance driver for OAR auto-contouring. Small high-consistency datasets enable optimal native model accuracy, whereas fine-tuning large pre-trained models with targeted physician data closes domain adaptation gaps and delivers state-of-the-art segmentation, advancing precision radiotherapy for head and neck oncology.​.