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Abstract Background There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. Methods Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. Results For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. Conclusions Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.

Abstract Background Given the absence of consensus diagnostic criteria for giant cell arteritis, clinicians may encounter difficulty with identification of new-onset headache in patients older than age 50 years presenting with visual changes and elevated inflammatory markers, particularly if temporal artery biopsies are performed and negative. Case presentation We present a case of a 57-year-old white man with headache, diplopia, and jaw paresthesia initially diagnosed and managed as steroid-refractory biopsy-negative giant cell arteritis. Further investigation disclosed evidence of soft tissue infiltration into Meckel’s (trigeminal) cave bilaterally. Positron emission tomography suggested the presence of a lymphoproliferative disorder. Histology confirmed the diagnosis of diffuse large B cell lymphoma. Conclusions Metastatic involvement in Meckel’s cave in diffuse large B cell lymphoma is extremely rare and presents a diagnostic challenge. Patients with suspicion of giant cell arteritis should undergo advanced imaging, particularly those with negative biopsy, atypical features, or lack of response to standard therapy, in order to assess for the presence of large-vessel vasculitis or other mimicking pathologies.