Brain Metabolic Alterations in Seropositive Autoimmune Encephalitis: An 18F-FDG PET Study

Role of F-18 FDG PET-CT in neuropsychiatric systemic lupus erythematosus

BACKGROUND

Neuropsychiatric systemic lupus erythematosus (NPSLE) is a major contributor to morbidity and mortality in systemic lupus erythematosus (SLE) patients. To date no single clinical, laboratory or imaging test has proven accurate for NPSLE diagnosis which is a testament to the intricate and multifactorial pathophysiological mechanisms suspected to exist. Functional imaging with FDG PET-CT has shown promise in NPSLE diagnosis, detecting abnormalities prior to changes evident on anatomical imaging. Research indicates that NPSLE may be more aggressive in people of African descent with higher mortality rates, making rapid and correct diagnosis even more important in the African context.

METHODS

In this narrative review, we provide a thorough appraisal of the current literature on the role of FDG PET-CT in NPSLE. Large, well-known databases were searched using appropriate search terms. Manual searches of references of retrieved literature were also included.

FINDINGS

A total of 73 article abstracts were assessed, yielding 26 papers that were directly relevant to the topic of FDG PET-CT in NPSLE. Results suggest that FDG PET-CT is a sensitive imaging test for NPSLE diagnosis and may play a role in assessing treatment response. It is complementary to routine anatomical imaging, particularly in diffuse manifestations of the disease. Newer quantitative analyses are commonly used for interpretation and can detect even subtle abnormalities, missed on visual inspection. Findings of group-wise analyses of FDG PET-CT scans in NPSLE patients are important in furthering our understanding of the complicated pathophysiological mechanisms involved. Limitations of FDG PET-CT include its lack of specificity, high cost and poor access.

CONCLUSION

FDG PET-CT is a sensitive test for NPSLE diagnosis but is hampered by lack of specificity. It is a valuable tool for clinicians managing SLE patients, particularly when anatomical imaging is negative. Its exact application will depend on the local context and clinical scenario.

Clinical characteristics and prognosis in patients with neuronal surface antibody-mediated autoimmune encephalitis: a single-center cohort study in China

Objective

This retrospective observational study primarily aimed to analyse the clinical characteristics of patients with neuronal surface antibody-mediated autoimmune encephalitis (AE) in China and report their prognosis after immunotherapy.

Methods

Clinical characteristics, laboratory or imaging examinations, and treatment outcomes of 103 patients diagnosed with AE between 1 September 2014 and 31 December 2020 were collected. Univariate and multivariate logistic regression analyses were performed to determine the predictors of poor prognosis.

Results

Overall, 103 patients were enrolled in the study. The main clinical symptoms included seizures (74.8%), psychiatric and behavior disorders (66.0%), cognitive deficits (51.5%), disturbances of consciousness (45.6%), and movement disorders/involuntary movements (26.2%). The distribution of clinical syndromes also differed for different AE subtypes. The efficacy rates of first-line immunotherapy for anti-NMDAR, anti-LGI1, anti-GABABR, and anti-CASPR2 encephalitis were 70.2%, 92.3%, 70%, and 83.3%, respectively, and rituximab was administered to 21 patients as second-line immunotherapy, including 14 patients with anti-NMDAR encephalitis, 4 with anti-LGI1 encephalitis, 2 with anti-GABABR encephalitis, and 1 with anti-CASPR2 encephalitis. Five patients with poor effect of the second-line treatment received bortezomib. According to the results of the last follow-up, 78 patients had a good prognosis (mRS 0-2), and 21 patients had a poor prognosis (mRS 3-6). The proportion of patients with a poor prognosis was significantly higher in anti-GABABR encephalitis compared to the other AE subtypes (p<0.001). Multivariate analysis indicated that elevated neutrophil-to-lymphocyte ratio (NLR) and tumour presence were independent risk factors for poor prognosis. The regression equation of the model was logit(P)=-3.480 + 0.318 NLR+2.434 with or without tumour (with assignment =1, without assignment =0). The prediction probability generated by the regression model equation was used as the independent variable for receiver operating curve (ROC) analysis. The results showed that the area under the curve (AUC) of the prediction probability was 0.847 (95% CI, 0.733-0.961; p < 0.001).

Conclusions

Different AE subtypes demonstrated different clinical symptom spectra throughout the disease stage. Anti-LGI1 encephalitis and anti-CASPR2 encephalitis were more sensitive to first-line and second-line treatments. Anti-GABABR encephalitis had the worst prognosis among the abovementioned subtypes. The regression equation constructed using NLR and tumour presence effectively predicted the poor prognosis.

A novel classification model based on cerebral 18F-FDG uptake pattern facilitates the diagnosis of acute/subacute seropositive autoimmune encephalitis

PURPOSE

To explore the intrinsic alteration of cerebral 18F-FDG metabolism in acute/subacute seropositive autoimmune encephalitis (AE) and to propose a universal classification model based on 18F-FDG metabolic patterns to predict AE.

METHODS

Cerebral 18F-FDG PET images of 42 acute/subacute seropositive AE patients and 45 healthy controls (HCs) were compared using voxelwise and region of interest (ROI)-based schemes. The mean standardized uptake value ratios (SUVRs) of 59 subregions according to a modified Automated Anatomical Labeling (AAL) atlas were compared using a t-test. Subjects were randomly divided into a training set (70%) and a testing set (30%). Logistic regression models were built based on the SUVRs and the models were evaluated by determining their predictive value in the training and testing sets.

RESULTS

The 18F-FDG uptake pattern in the AE group was characterized by increased SUVRs in the brainstem, cerebellum, basal ganglia, and temporal lobe, and decreased SUVRs in the occipital, and frontal regions with voxelwise analysis (false discovery rate [FDR] p<0.05). Utilizing ROI-based analysis, we identified 15 subareas that exhibited statistically significant changes in SUVRs among AE patients compared to HC (FDR p<0.05). Further, a logistic regression model incorporating SUVRs from the calcarine cortex, putamen, supramarginal gyrus, cerebelum_10, and hippocampus successfully enhanced the positive predictive value from 0.76 to 0.86 when compared to visual assessments. This model also demonstrated potent predictive ability, with AUC values of 0.94 and 0.91 observed for the training and testing sets, respectively.

CONCLUSIONS

During the acute/subacute stages of seropositive AE, alterations in SUVRs appear to be concentrated within physiologically significant regions, ultimately defining the general cerebral metabolic pattern. By incorporating these key regions into a new classification model, we have improved the overall diagnostic efficiency of AE.