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

Introduction: Amyloid-beta (Aß) 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 Aß-negative AD or Aß-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.

Quantitative comparative analysis of amyloid PET images using three radiopharmaceuticals.

OBJECTIVE: Amyloid positron emission tomography (PET) with F-18 florbetaben (FBB), F-18 flutemetamol (FMM), and F-18 florapronol (FPN) is being used clinically for the evaluation of dementia. These radiopharmaceuticals are commonly used to evaluate the accumulation of beta-amyloid plaques in the brain, but there are structural differences between them. We investigated whether there are any differences in the imaging characteristics. METHODS: A total of 605 subjects were enrolled retrospectively in this study, including healthy subjects (HS) and patients with mild cognitive impairment or Alzheimer's disease. Participants underwent amyloid PET imaging using one of the three radiopharmaceuticals. The PET images were analyzed visually and semi-quantitatively using a standardized uptake value ratio (SUVR). In addition, we calculated and compared the cut-off SUVR of the representative regions for each radiopharmaceutical that can distinguish between positive and negative scans. RESULTS: In the negative images of the HS group, the contrast between the white matter and the gray matter was high in the FMM PET images, while striatal uptake was relatively higher in the FPN PET images. The SUVR showed significant differences across the radiopharmaceuticals in all areas except the temporal lobe, but the range of differences was relatively small. Accuracy levels for the global cut-off SUVR to discriminate between positive and negative images were highest in FMM PET, with a value of 0.989. FBB PET also showed a high value of 0.978, while FPN PET showed a relatively low value of 0.901. CONCLUSIONS: Negative amyloid PET images using the three radiopharmaceuticals showed visually and quantitatively similar imaging characteristics except in the striatum. Binary classification using the cut-off of the global cortex showed high accuracy overall, although there were some differences between the three PET images.

Bibliometric analysis of research relating to the use of herbal medicine for rheumatoid arthritis between 1991 to 2021.

BACKGROUND: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease, which is well known for its poor symptoms, and the incidence is increasing worldwide. Although conventional medicines are practiced initially, patients often move or accompany to complementary and alternative medicines for better outcomes. Herbal medications, one of traditional medicinal approach, are widely used in clinical conditions. However, no research has broadly reviewed the current research trends regarding the scope of herbal medicines in RA. The aim of this study was to conduct a bibliometric analysis of applications of herbal medicine for RA from 1991 to the present. METHODS: We retrieved literature on herbal medicine for RA from the Web of Science database from 1991 to 2021. The VOSviewer program was used to analyze keywords, authors, countries, and organizations through visual knowledge mapping to assess the research hotspots and trends. RESULTS: A total of 516 articles were included in the analysis, which showed an increasing trend in the number of publications over time. Four clusters were identified from the keyword analysis: complementary and alternative medicine cluster, mechanism of the pain cluster, control for the pain cluster, and recent research trend cluster. People's Republic of China published 250 articles, which was the greatest number of publications, followed by the United States of America (82 articles) and South Korea (52 articles). Furthermore, Moudgil, KD of the University of Maryland published 15 articles about the mechanism of herbal medicine on autoimmune arthritis and their interaction with various drugs. Lu, AP of the Hong Kong Baptist University published 14 articles about herbal medicine interactions. On an institutional level, the China Academy of Chinese Medical Science published 33 articles, followed by the Hong Kong Baptist University and the University of Maryland with 27 and 20 articles, respectively. Clusters of organizations from the People's Republic of China and the United States of America revealed higher average citations for earlier average publication years. CONCLUSIONS: This bibliometric study identified trends in herbal medicine for RA from 1991 to 2021, which may guide the hot topics and future directions in this research field.

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer's Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depending on their structure: Inception3D, ResNet3D, and VGG3D. After training with 80% of the DAUH dataset, an appropriate model was selected, and the rest of the DAUH dataset was used for model evaluation. The generalization potential of the selected model was then validated using the external dataset. The accuracy of the model evaluation for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, and the accuracy of the external validation was 76.7%, 67.1%, and 85.3%, respectively. Inception3D and ResNet3D were retrained with the external dataset; then, the area under the curve was compared to determine the binary classification performance with a significance level of less than 0.05. When external validation was performed again after fine tuning, the performance improved to 15.3%p for Inception3D and 16.9%p for ResNet3D. In [18F]Florbetaben amyloid brain PET scan classification using CNN, the generalization potential can be seen through external validation. When there is a significant difference between the model classification performance and the external validation, changing the model structure or fine tuning the model can help improve the classification performance, and the optimal model can also be found by collaborating through a web-based open platform.

Heterogeneity by global and textural feature analysis in F-18 FP-CIT brain PET images for diagnosis of Parkinson's disease.

BACKGROUND: The quantification of heterogeneity for the striatum and whole brain with F-18 FP-CIT PET images will be useful for diagnosis. The index obtained from texture analysis on PET images is related to pathological change that the neuronal loss of the nigrostriatal tract is heterogeneous according to the disease state. The aim of this study is to evaluate various heterogeneity indices of F-18 FP-CIT PET images in the diagnosis of Parkinson's disease (PD) patients and to access the diagnostic accuracy of the indices using machine learning (ML). METHODS: This retrospective study included F-18 FP-CIT PET images of 31 PD and 31 age-matched health controls (HC). The volume of interest was delineated according to iso-contour lines around standardized uptake value (SUV) 3.0 g/ml for each region of the striatum by PMod 3.603. One hundred eight heterogeneity indices were calculated using CGITA to find indices from which the PD and HC were classified using statistical significance. PD group was classified by constructing a 2-dimensional or 3-dimensional phase space quantifier using these heterogeneity indices. We used 71 heterogeneity indices to classify PD from HC using ML for dimensional reduction. RESULTS: The heterogeneity indices for classifying PD from HC were size-zone variability, contrast, inverse difference-moment, and homogeneity in the order of low P value. Three-dimensional quantifiers composed of normalized-contrast, code-similarity, and contrast were more clearly classified than 2-dimensional ones. After 71-dimensional reduction using PCA, classification was possible by logistic regression with 91.3% accuracy. The 2 groups were classified with an accuracy of 85.5% using the support vector machine and 88.4% using the random forest. The classification accuracy using the eXtreme Gradient Boosting was 95.7%, and feature importance was highest in order of SUV bias-corrected kurtosis, size-zone-variability, intensity-variability, and high-intensity-zone-variability. CONCLUSION: It was confirmed that PD patients is more clearly classified than the conventional 2-dimensional quantifier by introducing a 3-dimensional phase space quantifier. We observed that ML can be used to classify the 2 groups in an easy and explanatory manner. For the discrimination of the disease, 24 heterogeneity indices were found to be statistically useful, and the major cut-off values of 3 heterogeneity indices were size-zone variability (1906.44), intensity variability (129.21), and high intensity zone emphasis (800.29).

Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.

Osteosarcoma of talus with heterotopic ossifications and lung metastases.

Osteosarcoma is the most common malignant bone tumor and is known to occur mainly in the metaphyses of long bones. However, a few cases of osteosarcoma in talus have been reported in older patients. We experienced an osteosarcoma of an 80-year-old male patient with a talus which is rarely reported and evaluated disease patterns with four different imaging modalities.

Relationship between the washout rate of I-123 MIBG scans and autonomic function in Parkinson's disease.

PURPOSE: We have evaluated the clinical significance of the washout rate (WR) on I-123 MIBG scans through the analysis of the relationship between the I-123 MIBG scans and autonomic status in patients with Parkinson's disease (PD). MATERIALS AND METHODS: Sixty patients with clinical PD who had decreased HMR were enrolled. An autonomic symptom was evaluated using a head-up tilt test and the Composite Autonomic Severity Score (CASS). An I-123 MIBG scan and F-18 FP-CIT positron emission tomography (PET) were performed. All of the patients were classified into three groups according to the WR. The differences in patient characteristics and the imaging parameters among the three groups were evaluated, and a correlation analysis was also performed. RESULTS: The frequency of orthostatic hypotension was significantly different among the three groups. The difference in systolic pressure (dSysPr) and the difference in diastolic pressure (dDiaPr) of group 3 was significantly larger than those of groups 1 and 2. From the correlation analysis, it can be seen that age, Hoehn and Yahr (H&Y) stage, dSysPr, and dDiaPr had a weak positive correlation with the WR. The total CASS score was significantly higher in group 3 compared with groups 1 and 2. The WR had a moderate positive correlation with the cardiosympathetic score and the total CASS score. CONCLUSION: The WR is related to autonomic dysfunction. An I-123 MIBG cardiac scan is considered to be a good method to evaluate not only the differential diagnosis of Parkinson's disease but also the degree of autonomic dysfunction.

Cerebral Amyloid Quantification in Cognitively Normal Korean Adults Using F-18 Florbetaben PET.

PURPOSE: To investigate regional cerebral amyloid beta retention in cognitively normal Korean adults using F-18 florbetaben (FBB). METHODS: We prospectively analyzed F-18 FBB positron emission tomography (PET)/CT scans of 30 cognitively healthy adults (age range, 50-70 years) using automated quantification. The standardized uptake value ratios (SUVRs) of F-18 FBB were calculated for predefined regions by normalizing the regional count with cerebellar cortex. RESULTS: The distribution of amyloid beta for each brain region revealed no age-related trends (p > 0.05). From all subjects, mean SUVR of amyloid deposit was 1.30 ± 0.18. The right parietal lobe showed the highest SUVR value (1.46 ± 0.23), whereas the right frontal lobe and left precuneus showed the lowest SUVR (1.23 ± 0.25). CONCLUSIONS: We provide reference values of normative data obtained from healthy elderly Koreans and suggest its use for accurate diagnosis of patients with Alzheimer's disease.

Correlation of Early-Phase F-18 Florapronal PET with F-18 FDG PET in Alzheimer's Disease and Normal Brain.

PURPOSE: F-18 florapronol (FPN) is the commercially recognized beta-amyloid positron emission tomography (PET) radiotracer in Korea. This study compared the early F-18 florapronol PET with F-18 fluorodeoxyglucose (FDG) PET between healthy controls (HC) and Alzheimer's dementia (AD) patients. METHODS: A total of 29 subjects (15 HC and 14 AD subjects) underwent F-18 FPN PET and F-18 FDG PET. F-18 FDG PET image was acquired from 30 to 60 min and F-18 FPN PET for 0 to 10 min. F-18 FPN and F-18 FDG images were spatially normalized with transformation matrices obtained from individual CT images and standardized uptake value ration (SUVR) from cerebellum area, and the global mean was calculated using PMOD 3.6. Pearson's correlation coefficients between F-18 FDG and early F-18 FPN for predefined cortical brain regions were calculated. RESULTS: We compared the F-18 FDG and F-18 FPN for SUVR of a specific region in global mean normalization and cerebellum normalization, and most of the correlation coefficient was higher in global mean normalization. In global mean normalization, the correlation coefficient for SUVR of HC was higher than that of AD in all brain regions. CONCLUSIONS: Early F-18 FPN study can be used as a proxy marker for the F-18 FDG PET.

Effect of 12-week home-based cognitive training on cognitive function and brain metabolism in patients with amnestic mild cognitive impairment.

PURPOSE: We assessed the effect of home-based cognitive intervention (HCI) on cognitive function along with brain metabolism by 18F-FDG PET in patients with amnestic MCI (aMCI). PATIENTS AND METHODS: Fifty-seven patients with aMCI from three hospitals were randomized (30 HCI, 27 control). For 12 weeks, subjects received HCI. Thirty-two subjects (15 HCI, 17 control) underwent brain 18-F-FDG-PET imaging at baseline and at 12 and 24 weeks. RESULTS: The HCI group showed significant improvement in the scores of the Controlled Oral Word Association Test (COWAT) 12 and at 24 weeks. Significant brain metabolic changes by 18F-FDG PET were not observed. CONCLUSION: The current study suggests that HCI was effective in improving general cognition along with frontal executive function in patients with aMCI.

Prediction of Alzheimer's Pathological Changes in Subjective Cognitive Decline Using the Self-report Questionnaire and Neuroimaging Biomarkers.

BACKGROUND AND PURPOSE: Subjective cognitive decline (SCD) may be the first symptomatic stage of Alzheimer's disease (AD). Hence, a screening tool to characterize the patients' complaints and assess the risk of AD is required. We investigated the SCD neuroimaging biomarker distributions and the relevance between the self-report questionnaire and Alzheimer's pathologic changes. METHODS: Individuals aged 50 and above with consistent cognitive complaints without any objective cognitive impairments were eligible for the study. The newly developed questionnaire consisted of 2 parts; 10 questions translated from the 'SCD-plus criteria' and a Korean version of the cognitive failure questionnaire by Broadbent. All the subjects underwent physical examinations such as blood work, detailed neuropsychological tests, the self-report questionnaire, brain magnetic resonance imagings, and florbetaben positron emission tomography (PET) scans. Amyloid PET findings were interpreted using both visual rating and quantitative analysis. Group comparisons and association analysis were performed using SPSS (version 18.0). RESULTS: A total of 31 participants with SCD completed the study and 25.8% showed positive amyloid depositions. The degree of periventricular white matter hyperintensities (WMH) and hippocampal atrophy were more severe in amyloid-positive SCDs compared to the amyloid-negative group. In the self-reported questionnaire, the 'informant's report a decline' and 'symptom's onset after 65 years of age' were associated with more Alzheimer's pathologic changes. CONCLUSIONS: Amyloid-positive SCDs differed from amyloid-negative SCDs on WMH, hippocampal atrophy, and a few self-reported clinical features, which gave clues on the prediction of AD pathology.

The Effect of Clinical Characteristics and Subtypes on Amyloid Positivity in Patients with Amnestic Mild Cognitive Impairment.

BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) is a prodromal stage of dementia. Amyloid deposits in positron-emission tomography (PET) imaging of MCI patients imply a higher risk for advancing to dementia, with rates of 10%-15% yearly. The purpose of this study was to investigate the clinical characteristics of subgroups of amnestic MCI (aMCI) that may have a higher impact on amyloid positivity. METHODS: We recruited 136 aMCI patients. All patients underwent a 20-minute F-18 florbetaben or flutemetamol PET scan. We classified amyloid PET images as positive or negative according to a semi-quantitative method. We evaluated the amyloid positivity of subgroups of aMCI (early vs. late type, single vs. multiple amnestic type, verbal vs. verbal, and visual amnestic type), and compared baseline clinical characteristics including key risk factors, apolipoprotein E4 (apoE4) genotype, and neuropsychological assessments with amyloid positivity in aMCI. RESULTS: The amyloid positivity in total aMCI was 41%. The positivity rate according to subgroup of aMCI were as follow: Late aMCI (49%) vs. early aMCI (33%) (p=0.13), multiple aMCI (40%) vs. single aMCI (38%) (p=0.51), and verbal and visual aMCI (59%) vs. verbal aMCI (35%) (p=0.01), respectively. The mean age and the frequency of apoE4 allele of the amyloid-positive group was higher than that of the amyloid-negative group in aMCI (p<0.01). CONCLUSIONS: We found that the amyloid positivity was related to patterns of clinical subtypes, characteristics, and risk factors in patients with aMCI.

Role of Positron Emission Tomography as a Biologic Marker in the Diagnosis of Primary Progressive Aphasia: Two Case Reports.

Primary progressive aphasia (PPA) is a heterogenous neurodegenerative disorder characterized by declining language and speech ability. Various underlying neuropathologies can induce PPA, and the disorder is divided into three subtypes-progressive non-fluent aphasia, semantic variant aphasia, and logopenic aphasia-according to clinical features. Accurate disease classification and prediction of underlying diseases are necessary for appropriate treatment, but proper use of imaging tests is important because clinical information alone often makes it difficult to make accurate decisions. Because there is a characteristic metabolic pattern according to the subtypes, F-18 fluorodeoxyglucose positron emission tomography (PET) can indicate subtype classification. In addition, PET studies for imaging amyloid or dopamine transporters play an important role in demonstrating underlying disease. The present case showed that PET imaging studies are useful in diagnosis and could be used as a biomarker in PPA.

A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment.

BACKGROUND: Most clinical trials focus on amyloid-ß positive (Aß+) amnestic mild cognitive impairment (aMCI), but screening failures are high because only a half of patients with aMCI are positive on Aß PET. Therefore, it becomes necessary for clinicians to predict which patients will have Aß biomarker. OBJECTIVE: We aimed to compare clinical factors, neuropsychological (NP) profiles, and apolipoprotein E (APOE) genotype between Aß+ aMCI and Aß-aMCI and to develop a clinically useful prediction model of Aß positivity on PET (PET-Aß+) in aMCI using a nomogram. METHODS: We recruited 523 aMCI patients who underwent Aß PET imaging in a nation-wide multicenter cohort. The results of NP measures were divided into following subgroups: 1) Stage (Early and Late-stage), 2) Modality (Visual, Verbal, and Both), 3) Recognition failure, and 4) Multiplicity (Single and Multiple). A nomogram for PET-Aß+ in aMCI patients was constructed using a logistic regression model. RESULTS: PET-Aß+ had significant associations with NP profiles for several items, including high Clinical Dementia Rating Scale Sum of Boxes score (OR 1.47, p = 0.013) and impaired memory modality (impaired both visual and verbal memories compared with visual only, OR 3.25, p = 0.001). Also, presence of APOEɛ4 (OR 4.14, p < 0.001) was associated with PET-Aß+. These predictors were applied to develop the nomogram, which showed good prediction performance (C-statistics = 0.79). Its prediction performances were 0.77/0.74 in internal/external validation. CONCLUSIONS: The nomogram consisting of NP profiles, especially memory domain, and APOEɛ4 genotype may provide a useful predictive model of PET-Aß+ in patients with aMCI.

Correction to: a risk stratification model for nodal peripheral T-cell lymphomas based on the NCCN-IPI and posttreatment Deauville score.

Unfortunately, the original version of this article contained several errors made during final step of article production. In the results section (fourth sentence) of the Abstract, the incomplete sentence,", 31.4% in high-risk group and 4.7% in treatment failure group.

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

A risk stratification model for nodal peripheral T-cell lymphomas based on the NCCN-IPI and posttreatment Deauville score.

PURPOSE: The aim of this study was to establish a risk-stratification model integrating posttreatment metabolic response using the Deauville score and the pretreatment National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) in nodal PTCLs. METHODS: We retrospectively analysed 326 patients with newly diagnosed nodal PTCLs between January 2005 and June 2016 and both baseline and posttreatment PET/CT data. The final model was validated using an independent prospective cohort of 79 patients. RESULTS: Posttreatment Deauville score (1/2, 3, and 4/5) and the NCCN-IPI (low, low-intermediate, high-intermediate, and high) were independently associated with progression-free survival: for the Deauville score, the hazard ratios (HRs) were 1.00 vs. 2.16 (95% CI 1.47-3.18) vs. 7.86 (5.66-10.92), P < 0.001; and for the NCCN-IPI, the HRs were 1.00 vs. 2.31 (95% CI 1.20-4.41) vs. 4.42 (2.36-8.26) vs. 7.09 (3.57-14.06), P < 0.001. Based on these results, we developed a simplified three-group risk model comprising a low-risk group (low or low-intermediate NCCN-IPI with a posttreatment Deauville score of 1 or 2, or low NCCN-IPI with a Deauville score of 3), a high-risk group (high or high-intermediate NCCN-IPI with a Deauville score of 1/2 or 3, or low-intermediate NCCN-IPI with a Deauville score of 3), and a treatment failure group (Deauville score 4 or 5). This model was significantly associated with progression-free survival (5-year, 70.3%, 31.4%, and 4.7%; P < 0.001) and overall survival (5-year, 82.1%, 45.5%, and 14.7%; P < 0.001). Similar associations were also observed in the independent validation cohort. CONCLUSION: The risk-stratification model integrating posttreatment Deauville score and pretreatment NCCN-IPI is a powerful tool for predicting treatment failure in patients with nodal PTCLs.

Assessment of brain beta-amyloid deposition in transgenic mouse models of Alzheimer's disease with PET imaging agents 18F-flutemetamol and 18F-florbetaben.

BACKGROUND: Although amyloid beta (Aß) imaging is widely used for diagnosing and monitoring Alzheimer's disease in clinical fields, paralleling comparison between 18F-flutemetamol and 18F-florbetaben was rarely attempted in AD mouse model. We performed a comparison of Aß PET images between 18F-flutemetamol and 18F-florbetaben in a recently developed APPswe mouse model, C57BL/6-Tg (NSE-hAPPsw) Korl. RESULTS: After an injection (0.23 mCi) of 18F-flutemetamol and 18F-florbetaben at a time interval of 2-3 days, we compared group difference of SUVR and kinetic parameters between the AD (n = 7) and control (n = 7) mice, as well as between 18F-flutemetamol and 18F-florbetaben image. In addition, bio-distribution and histopathology were conducted. With visual image and VOI-based SUVR analysis, the AD group presented more prominent uptake than did the control group in both the 18F-florbetaben and 18F-flutemetamol images. With kinetic analysis, the 18F-florbetaben images showed differences in K1 and k4 between the AD and control groups, although 18F-flutemetamol images did not show significant difference. 18F-florbetaben images showed more prominent cortical uptake and matched well to the thioflavin S staining images than did the 18F-flutemetamol image. In contrast, 18F-flutemetamol images presented higher K1, k4, K1/k2 values than those of 18F-florbetaben images. Also, 18F-flutemetamol images presented prominent uptake in the bowel and bladder, consistent with higher bio-distribution in kidney, lung, blood and heart. CONCLUSIONS: Compared with 18F-flutemetamol images, 18F-florbetaben images showed prominent visual uptake intensity, SUVR, and higher correlations with the pathology. In contrast, 18F-flutemetamol was more actively metabolized than was 18F-florbetaben (Son et al. in J Nucl Med 58(Suppl 1):S278, 2017].

Pattern of cerebral hypoperfusion according to the clinical staging in dementia with Lewy bodies.

This study aimed to detect different patterns of cerebral hypoperfusion in DLB according to clinical staging. Thirty-three patients with DLB were recruited by clinical dementia rating (CDR) stage. Compared with control, cerebral hypoperfusion was mainly observed in the lingual gyrus, the cuneus, the occipital gyrus in CDR 0.5 group; the fusiform gyrus, the middle temporal gyrus, and the posterior cingulate in CDR 1; and the lingual gyrus, the cuneus, the hippocampus, the fusiform gyrus, and the inferior frontal gyrus in CDR 2. Our findings suggest that cerebral hypoperfusion spreads to the frontal cortex and temporal lobes as disease progresses.