Assessment of Defacing Techniques on Medical Images for Radiation Therapy: Implications for Patient Privacy and Data Utility

PURPOSE: To assess the impact of defacing-based deidentification techniques on reidentification risk and data utility across multimodal imaging in radiation therapy. METHODS AND MATERIALS: We applied 4 defacing techniques: biometricₘask, quickshear, mriᵣeface, and Carina's deidentifier, to imaging from 88 brain patients (magnetic resonance imaging, computed tomography CT, and RTDose) and 97 head and neck patients (positron emission tomography, CT, and RTDose) in The Cancer Imaging Archive. Reidentification risk was assessed using ArcFace, a deep learning-based facial recognition model, by measuring cosine similarity scores and conducting receiver operating characteristic analysis to distinguish between original and defaced images. Data integrity was evaluated by statistically comparing the volume and image intensity changes between the original and defaced images across 9 critical organs and gross tumor volume. RESULTS: by 2. 11 Gy (IQR, 0. 00 Gy to 3. 39 Gy) and 1. 05 Gy (IQR, 0. 21 Gy to 1. 16 Gy), respectively. A similar trend was observed in the head and neck data set with larger deviations. CONCLUSIONS: Carina's deidentifier and mriᵣeface showed favorable privacy-utility trade-offs relative to facial removal; the optimal choice may vary by application priorities.