Mimics of Autoimmune Encephalitis: Validation of the 2016 Clinical Autoimmune Encephalitis Criteria.

BACKGROUND AND OBJECTIVES: The clinical criteria for autoimmune encephalitis (AE) were proposed by Graus et al. in 2016. In this study, the AE criteria were validated in the real world, and common AE mimics were described. In addition, criteria for probable anti-LGI1 encephalitis were proposed and validated. METHODS: In this retrospective cohort study, patients referred to our national referral center with suspicion of AE and specific neuroinflammatory disorders with similar clinical presentations were included from July 2016 to December 2019. Exclusion criteria were pure cerebellar or peripheral nerve system disorders. All patients were evaluated according to the AE criteria. RESULTS: In total, 239 patients were included (56% female; median age 42 years, range 1-85). AE was diagnosed in 104 patients (44%) and AE mimics in 109 patients (46%). The most common AE mimics and misdiagnoses were neuroinflammatory CNS disorders (26%), psychiatric disorders (19%), epilepsy with a noninflammatory cause (13%), CNS infections (7%), neurodegenerative diseases (7%), and CNS neoplasms (6%). Common confounding factors were mesiotemporal lesions on brain MRI (17%) and false-positive antibodies in serum (12%). Additional mesiotemporal features (involvement extralimbic structures, enhancement, diffusion restriction) were observed more frequently in AE mimics compared with AE (61% vs 24%; p = 0.005). AE criteria showed the following sensitivity and specificity: possible AE, 83% (95% CI 74-89) and 27% (95% CI 20-36); definite autoimmune limbic encephalitis (LE), 10% (95% CI 5-17) and 98% (95% CI 94-100); and probable anti-NMDAR encephalitis, 50% (95% CI 26-74) and 96% (95% CI 92-98), respectively. Specificity of the criteria for probable seronegative AE was 99% (95% CI 96-100). The newly proposed criteria for probable anti-LGI1 encephalitis showed a sensitivity of 66% (95% CI 47-81) and specificity of 96% (95% CI 93-98). DISCUSSION: AE mimics occur frequently. Common pitfalls in AE misdiagnosis are mesiotemporal lesions (predominantly with atypical features) and false-positive serum antibodies. As expected, the specificity of the criteria for possible AE is low because these criteria represent the minimal requirements for entry in the diagnostic algorithm for AE. Criteria for probable AE (-LGI1, -NMDAR, seronegative) and definite autoimmune LE are applicable for decisions on immunotherapy in early disease stage, as specificity is high.

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.

PURPOSE: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI. EXPERIMENTAL DESIGN: Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images. RESULTS: The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83). CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable.

[Diagnostic imaging in dementia: use of imaging modalities in Dutch memory clinics]. / Beeldvormende diagnostiek bij dementie: inventarisatie van de praktijk binnen de Nederlandse geheugenklinieken.

PURPOSE: To evaluate the use of MRI and CT in the diagnostic work-up of dementia in Dutch memory clinics, and to analyse the rationale for choosing each modality. MATERIALS AND METHODS: A digital survey was sent by e-mail to all medical specialists (n=235) working at a memory clinic in the Netherlands. RESULTS: The survey was completed by 64% (151). 85% of the respondents were geriatricians, 13% neurologists and 2% other, working at a total of 69 clinics. 40% variably orders CT or MRI, 37% orders MRI, 19% CT, and 4% CT plus MRI. Primary factors influencing this choice are: MRI contraindications, physical limitations, age, vascular or oncological medical history, and waiting time. With CT, 87% indicates information is lacking: vascular disease/white matter lesions, (hippocampal) atrophy, and specific pathologies (metastases, amyloid angiopathy). Furthermore, respondents prefer MRI because they can assess the images more easily themselves. Only 50% of respondents indicate that CT protocol dictates coronal reconstructions. Additionally, these reconstructions are not provided consistently. Rating-scales are used to describe images in 5%. In 75% assessment is not uniform. CONCLUSION: MRI is preferred over CT in diagnostic imaging of dementia, in accordance with existing guidelines. However, these guidelines are mostly out-dated and modern multislice CT potential is relatively unknown among geriatricians. In memory clinics, multislice CT could offer a well suitable imaging alternative, but only if multiplanar reconstructions are performed consistently. Furthermore, radiology reports need to be improved by using more standardized assessment.