Bayer AG.

Purpose: The metabolic patterns of 18 F-fluoro-2-deoxy-d-glucose positron emission tomography ( 18 F-FDG-PET) in autoimmune encephalitis associated with leucine-rich glioma-inactivated 1 antibody (LGI1 AE) are still unclear. We performed a cohort study to investigate the clinical metabolic characteristics and diagnostic value based on 18 F-FDG-PET in patients with LGI1 AE.Materials and Methods: A total of 34 patients including 18 patients (53%) in the acute phase and 16 patients (47%) in the chronic phase who were diagnosed with LGI1 AE were retrospectively analyzed from October 2014 to June 2018 at the Department of Neurology in Beijing Tiantan Hospital, the Capital Medical University. The clinical data were collected by searching through electronic medical records.Results: The initial 18 F-FDG-PET scan indicated a significant abnormal metabolic pattern in 31 LGI1 AE patients (91%), whereas only 20 patients (59%) showed an abnormal MRI signal (P < 0.05). The 18 F-FDG-PET metabolic pattern was reversible after treatment; most of the patients showed an almost normal uptake of 18 F-FDG-PET after discharge. Regarding the spatial distribution, the abnormal metabolic pattern in LGI1 AE subjects exhibiting hypermetabolism was specifically located in the basal ganglia (BG) and medial temporal lobe (MTL). BG hypermetabolism was observed in 28 subjects (82%), and 68% of patients showed MTL hypermetabolism. A total of 17 patients (50%) exhibited faciobrachial dystonic seizures (FBDS), and the remaining subjects showed non-FBDS symptoms (50 and 50%). BG-only hypermetabolism was detected in seven subjects in the FBDS subgroup (7/16) but in only one subject in the non-FBDS subgroup (1/15) (44 vs. 7%, P < 0.05). Conclusion:18 F-FDG-PET imaging was more sensitive than MRI in the diagnosis of LGI1 AE. Isolated BG hypermetabolism was more frequently observed in subjects with FBDS, suggesting the potential involvement of the BG.

Purpose To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients. Methods Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative 18 F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET), 11 C-methionine (11 C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients. Results The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of 18 F-FDG, maximum of TBR of 11 C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975-1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881-0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful. Conclusions Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.