Spatial Normalization Using Early-Phase [18F]FP-CIT PET for Quantification of Striatal Dopamine Transporter Binding

Neurofilament light chain and cardiac MIBG uptake as predictors for phenoconversion in isolated REM sleep behavior disorder

BACKGROUND

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is considered as a prodromal stage of either multiple system atrophy (MSA) or Lewy body disease (LBD; Parkinson's disease and dementia with Lewy bodies). However, current knowledge is limited in predicting and differentiating the type of future phenoconversion in iRBD patients. We investigated the role of plasma neurofilament light chain (NfL) and cardiac metaiodobenzylguanidine (MIBG) uptake as predictors for phenoconversion.

METHODS

Forty patients with iRBD were enrolled between April 2018 and October 2019 and prospectively followed every 3 months to determine phenoconversion to either MSA or LBD. Plasma NfL levels were measured at enrollment. Cardiac MIBG uptake and striatal dopamine transporter uptake were assessed at baseline.

RESULTS

Patients were followed for a median of 2.92 years. Four patients converted to MSA and 7 to LBD. Plasma NfL level at baseline was significantly higher in future MSA-converters (median 23.2 pg/mL) when compared with the rest of the samples (median 14.1 pg/mL, p = 0.003). NfL level above 21.3 pg/mL predicted phenoconversion to MSA with the sensitivity of 100% and specificity of 94.3%. Baseline MIBG heart-to-mediastinum ratio of LBD-converters (median 1.10) was significantly lower when compared with the rest (median 2.00, p < 0.001). Heart-to-mediastinum ratio below 1.545 predicted phenoconversion to LBD with the sensitivity of 100% and specificity of 92.9%.

CONCLUSIONS

Plasma NfL and cardiac MIBG uptake may be useful biomarkers in predicting phenoconversion of iRBD. Elevated plasma NfL levels may suggest imminent phenoconversion to MSA, whereas low cardiac MIBG uptake suggests phenoconversion to LBD.

Alzheimer’s Disease Prediction Using Attention Mechanism with Dual-Phase 18F-Florbetaben Images

Introduction

Amyloid-beta (Aβ) imaging test plays an important role in the early diagnosis and research of biomarkers of Alzheimer’s disease (AD) but a single test may produce Aβ-negative AD or Aβ-positive cognitively normal (CN). In this study, we aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via a deep learning–based attention method and evaluate the AD positivity scores compared to late-phase FBB which is currently adopted for AD diagnosis.

Materials and Methods

A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests, were retrospectively analyzed. Early- and delay-phase FBB images were spatially normalized with an in-house FBB template. The regional standard uptake value ratios were calculated with the cerebellar region as a reference region and used as independent variables that predict the diagnostic label assigned to the raw image.

Results

AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 0.858, AUROC: 0.831) than those from delay phase FBB imaging (ACC: 0.821, AUROC: 0.794). AD positivity score estimated by dual-phase FBB (R: −0.5412) shows a higher correlation with psychological test compared to only dFBB (R: −0.2975). In the relevance analysis, we observed that LSTM uses different time and regions of early-phase FBB for each disease group for AD detection.

Conclusions

These results show that the aggregated model with dual-phase FBB with long short-term memory and attention mechanism can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only a single phase FBB.