Dataset Information

Distinct cerebral ¹⁸F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

ABSTRACT:

Aim

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a subgroup of treatable autoimmune encephalitis, characterized by rapid development of psychosis, cognitive impairments and seizures. Etiologically, anti-NMDAR encephalitis could be divided into three subgroups, which are paraneoplastic (especially associated with ovarian teratoma), viral encephalitis-related and cryptogenic. Each type is different in clinical course, treatment strategies and prognosis. In this study, we aim to investigate whether anti-NMDAR encephalitis patients with different trigger factors exhibit distinct cerebral metabolic patterns detected by ¹⁸F-fluorodeoxyglucose positron emission tomography imaging.

Methods

24 patients with anti-NMDAR encephalitis in acute phase from Huashan Hospital, Fudan University (Shanghai, China) were recruited in this study. Each patient was classified into one of etiological subgroups. Positron emission tomography images of individual patients were analyzed with both routine visual reading and computer-supported reading by comparison with those of the same 10 healthy controls using a voxel-wise statistical parametric mapping analysis.

Results

Patients in both the cryptogenic (13 patients) and paraneoplastic (five patients) subgroups showed hypermetabolism in the frontal-temporal lobes and basal ganglia, covarying with hypometabolism in the occipital regions. Notably, the abnormal metabolism was usually asymmetric in the cryptogenic subgroup, but relatively symmetric in the paraneoplastic subgroup. Moreover, the other six patients secondary to viral encephalitis presented with significant hypometabolism in the bilateral occipital regions, as well as in the unilateral temporal lobes and part of basal ganglia (also is virus infection side), but hypermetabolism in the contralateral temporal areas.

Conclusion

This study revealed that patients with anti-NMDAR encephalitis triggered by different factors presented distinct cerebral metabolic patterns. Awareness of these patterns may help to better understand the varying occurrence and development of anti-NMDAR encephalitis in each subgroup, and could offer valuable information to the early diagnosis, treatment and prognosis of this disorder.

Trial registration number

ChiCTR2000029115 (Chinese clinical trial registry site, http://www.chictr.org).

SUBMITTER: Ge J

PROVIDER: S-EPMC7919218 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Distinct cerebral <sup>18</sup>F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

Ge Jingjie J Deng Bo B Guan Yihui Y Bao Weiqi W Wu Ping P Chen Xiangjun X Zuo Chuantao C

Therapeutic advances in neurological disorders 20210224

<h4>Aim</h4>Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a subgroup of treatable autoimmune encephalitis, characterized by rapid development of psychosis, cognitive impairments and seizures. Etiologically, anti-NMDAR encephalitis could be divided into three subgroups, which are paraneoplastic (especially associated with ovarian teratoma), viral encephalitis-related and cryptogenic. Each type is different in clinical course, treatment strategies and prognosis. In this study, we ...[more]

PMID: 33717212

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Project description:Knowledge of the within-subject variability of 18F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test-retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq 18F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUVmax, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%-12.8%) than PET/MRI (6.6%-8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUVpeak, the wCVs appeared similar for PET/CT (9.9%-11.5%) and PET/MRI (9.2%-11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADCmedian of 3.5% appeared lower than the wCVs for SUVmax (6.6%-8.7%) and SULpeak (9.2%-11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on 18F-FDG PET/MRI is both acceptably high and similar to previously published values for 18F-FDG PET/CT and MRI, supporting the use of 18F-FDG PET/MRI for quantitative oncologic treatment response assessments.

Project description:BackgroundQuantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.MethodsSimulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.ResultsSimulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.ConclusionsA model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

Dataset Information

Distinct cerebral ¹⁸F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

Aim

Methods

Results

Conclusion

Trial registration number

Publications

Distinct cerebral <sup>18</sup>F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Distinct cerebral 18F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

Aim

Methods

Results

Conclusion

Trial registration number

Publications

Distinct cerebral <sup>18</sup>F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Distinct cerebral ¹⁸F-FDG PET metabolic patterns in anti-N-methyl-D-aspartate receptor encephalitis patients with different trigger factors.