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Background Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants’ privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. Methods FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer’s Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 glioblastoma patients of the PICTURE Study Group, tumor volumes (GBV) were measured by BraTumIA. Failed analyses on FFR-processed images were recorded. Only cases in which all image analyses completed successfully were analyzed. Differences between outcomes obtained from FFR-processed and full images were assessed, by quantifying the intra-class correlation coefficient (ICC) for absolute agreement and by testing for systematic differences using paired t tests. Results Automated analysis methods failed in 0–19% of cases in FFR-processed images versus 0–2% of cases in full images. ICC for absolute agreement ranged from 0.312 (GBV after FaceMasking) to 0.998 (WMLV after Defacing). FaceMasking yielded higher NBV (p = 0.003) and WMLV (p ≤ 0.001). GBV was lower after QuickShear and Defacing (both p Conclusions All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences. Further study is warranted to ensure high-quality neuroimaging research while protecting participants’ privacy. Key Points • Protecting participants’ privacy when sharing MRI data is important. • Impact of three facial features removal methods on subsequent analysis was assessed in three clinical groups. • Removing facial features degrades performance of image analysis methods.

Different strategies have been developed to identify those refractory celiac disease (RCD) patients who are at risk to develop an enteropathy associated T-cell lymphoma (EATL). Flow cytometric analysis of intra-epithelial lymphocytes (IEL) with an aberrant phenotype is considered the golden standard but is not widely available. Immunohistochemistry (IHC) and T-cell receptor (TCR) rearrangement studies are commonly available but may lack sensitivity and specificity. Here, we compared the three different methods in the workup of patients suspected for RCD. Duodenal biopsies from control patient (n = 5), RCD patients with moderately increased aberrant IEL populations (20–50 %: n = 14), and RCD patients with high numbers of aberrant IEL (>50 %: n = 5) as determined by flow cytometry were analysed by IHC and TCR-γ chain rearrangement analysis. Three pathologists scored the slides independently. Sensitivity of IHC and TCR-γ rearrangement analysis in RCD patients with high numbers of aberrant IELs was 100 and 71 %, respectively. RCD patients with aberrant cells between 25 and 50 % however, were missed by IHC and TCR in 50 and 57 % of cases, respectively. In addition, inter-rater reliability analysis of the IHC scoring revealed coder-pair Kappa coefficients between 0.28 and 0.85. Immunohistochemistry and to a lesser extent TCR-γ clonality analysis are sensitive in identifying patients with high numbers of aberrant IEL populations, yet miss half of RCD patients with moderately increased numbers. In addition, IHC has a high inter-observer variability. Therefore, patients suspected for RCD should undergo flow cytometric analysis of the duodenum.