The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials.

To assess the impact of defacing-based deidentification techniques on reidentification risk and data utility across multimodal imaging in radiotherapy. We applied four defacing techniques: biometric_mask, quickshear, mri_reface, and Carina's deidentifier, to imaging from 88 brain patients (MRI, CT, and RTDose) and 97 head-and-neck (HN) patients (PET, CT, and RTDose) in The Cancer Imaging Archive (TCIA). Reidentification risk was assessed using ArcFace, a deep learning-based facial recognition model, by measuring cosine similarity scores and conducting receiver operating characteristic (ROC) analysis to distinguish between original and defaced images. Data integrity was evaluated by statistically comparing volume and image intensity changes between original and defaced images across nine critical organs and GTV. Quickshear provides the highest privacy protection, achieving the lowest AUC across imaging modalities (AUC: 0.61-0.74), followed by Carina (0.59-0.80). Mri_reface showed moderate protection (0.70-0.91), while biometric_mask offered the least (0.76-0.94). Carina preserved structure volumes, and mri_reface produced minor volumetric changes in the eyes (5%) and lens (9%). In contrast, biometric_mask substantially affected the mandible (39%) and oral cavity (69%), while quickshear significantly altered multiple structures (10.0%-86.6%). Median changes in mean CT intensity after defacing were -48.3% (IQR: 32%) with biometric_mask and -77.7% (IQR: 37.7%) with quickshear in the oral cavity. For eyes, Carina and mri_reface produced changes of +24.5% (IQR: 22.6%) and +54.9% (IQR: 66.1%), respectively. In the brain dataset, biometric_mask and quickshear decreased oral cavity D_mean by 0.72 Gy (IQR: 1.22) and 1.61 Gy (IQR: 2.84), respectively. Carina and mri_reface reduced Eyes D_max by 2.11 Gy (IQR: 3.44) and 1.05 Gy (IQR: 1.65), respectively. A similar trend was observed in the HN dataset with larger deviations. Carina's deidentifier and mri_reface showed favorable privacy-utility trade-offs relative to facial removal; the optimal choice may vary by application priorities.