Association of Glycemic Variability With Imaging Markers of Vascular Burden, ß-Amyloid, Brain Atrophy, and Cognitive Impairment.

BACKGROUND AND OBJECTIVE: We aimed to investigate the association between glycemic variability (GV) and neuroimaging markers of white matter hyperintensities (WMH), beta-amyloid (Aß), brain atrophy, and cognitive impairment. METHODS: This was a retrospective cohort study that included participants without dementia from a memory clinic. They all had Aß PET, brain MRI, and standardized neuropsychological tests and had fasting glucose (FG) levels tested more than twice during the study period. We defined GV as the intraindividual visit-to-visit variability in FG levels. Multivariable linear regression and logistic regression were used to identify whether GV was associated with the presence of severe WMH and Aß uptake with DM, mean FG levels, age, sex, hypertension, and presence of APOE4 allele as covariates. Mediation analyses were used to investigate the mediating effect of WMH and Aß uptake on the relationship between GV and brain atrophy and cognition. RESULTS: Among the 688 participants, the mean age was 72.2 years, and the proportion of female participants was 51.9%. Increase in GV was predictive of the presence of severe WMH (coefficient [95% CI] 1.032 [1.012-1.054]; p = 0.002) and increased Aß uptake (1.005 [1.001-1.008]; p = 0.007). Both WMH and increased Aß uptake partially mediated the relationship between GV and frontal-executive dysfunction (GV → WMH → frontal-executive; direct effect, -0.319 [-0.557 to -0.080]; indirect effect, -0.050 [-0.091 to -0.008]) and memory dysfunction (GV → Aß â†’ memory; direct effect, -0.182 [-0.338 to -0.026]; indirect effect, -0.067 [-0.119 to -0.015]), respectively. In addition, increased Aß uptake completely mediated the relationship between GV and hippocampal volume (indirect effect, -1.091 [-2.078 to -0.103]) and partially mediated the relationship between GV and parietal thickness (direct effect, -0.00101 [-0.00185 to -0.00016]; indirect effect, -0.00016 [-0.00032 to -0.000002]). DISCUSSION: Our findings suggest that increased GV is related to vascular and Alzheimer risk factors and neurodegenerative markers, which in turn leads to subsequent cognitive impairment. Furthermore, GV can be considered a potentially modifiable risk factor for dementia prevention.

Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET.

PURPOSE: The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional ß-amyloid (Aß) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aß pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. PATIENTS AND METHODS: We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. RESULTS: The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aß involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aß deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. CONCLUSIONS: The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.

ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: Bridging the AI Gap for Clinicians.

Background: Bone scans are often used to identify bone metastases, but their low specificity may necessitate further studies. Deep learning models may improve diagnostic accuracy but require both medical and programming expertise. Therefore, we investigated the feasibility of constructing a deep learning model employing ChatGPT for the diagnosis of bone metastasis in bone scans and to evaluate its diagnostic performance. Method: We examined 4626 consecutive cancer patients (age, 65.1 ± 11.3 years; 2334 female) who had bone scans for metastasis assessment. A nuclear medicine physician developed a deep learning model using ChatGPT 3.5 (OpenAI). We employed ResNet50 as the backbone network and compared the diagnostic performance of four strategies (original training set, original training set with 1:10 class weight, 10-fold data augmentation for positive images only, and 10-fold data augmentation for all images) to address the class imbalance. We used a class activation map algorithm for visualization. Results: Among the four strategies, the deep learning model with 10-fold data augmentation for positive cases only, using a batch size of 16 and an epoch size of 150, achieved the area under curve of 0.8156, the sensitivity of 56.0 %, and specificity of 88.7 %. The class activation map indicated that the model focused on disseminated bone metastases within the spine but might confuse them with benign spinal lesions or intense urinary activity. Conclusions: Our study illustrates that a clinical physician with rudimentary programming skills can develop a deep learning model for medical image analysis, such as diagnosing bone metastasis in bone scans using ChatGPT. Model visualization may offer guidance in enhancing deep learning model development, including preprocessing, and potentially support clinical decision-making processes.

Distinct effects of cholesterol profile components on amyloid and vascular burdens.

BACKGROUND: Cholesterol plays important roles in ß-amyloid (Aß) metabolism and atherosclerosis. However, the relationships of plasma cholesterol levels with Aß and cerebral small vessel disease (CSVD) burdens are not fully understood in Asians. Herein, we investigated the relationships between plasma cholesterol profile components and Aß and CSVD burdens in a large, non-demented Korean cohort. METHODS: We enrolled 1,175 non-demented participants (456 with unimpaired cognition [CU] and 719 with mild cognitive impairment [MCI]) aged ≥ 45 years who underwent Aß PET at the Samsung Medical Center in Korea. We performed linear regression analyses with each cholesterol (low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], and triglyceride) level as a predictor and each image marker (Aß uptake on PET, white matter hyperintensity [WMH] volume, and hippocampal volume) as an outcome after controlling for potential confounders. RESULTS: Increased LDL-c levels (ß = 0.014 to 0.115, p = 0.013) were associated with greater Aß uptake, independent of the APOE e4 allele genotype and lipid-lowering medication. Decreased HDL-c levels (ß = - 0.133 to - 0.006, p = 0.032) were predictive of higher WMH volumes. Increased LDL-c levels were also associated with decreased hippocampal volume (direct effect ß = - 0.053, p = 0.040), which was partially mediated by Aß uptake (indirect effect ß = - 0.018, p = 0.006). CONCLUSIONS: Our findings highlight that increased LDL-c and decreased HDL-c levels are important risk factors for Aß and CSVD burdens, respectively. Furthermore, considering that plasma cholesterol profile components are potentially modified by diet, exercise, and pharmacological agents, our results provide evidence that regulating LDL-c and HDL-c levels is a potential strategy to prevent dementia.

Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease.

Background and objectives: Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD. Methods: In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis. Results: We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia. Conclusion: Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.

Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens.

OBJECTIVES: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. METHODS: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. RESULTS: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. CONCLUSION: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.

Development and clinical validation of CT-based regional modified Centiloid method for amyloid PET.

BACKGROUND: The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aß) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aß uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). METHODS: For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer's disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). RESULTS: Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). CONCLUSIONS: Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.

Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images.

Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer's dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.

Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model.

Although skull-stripping and brain region segmentation are essential for precise quantitative analysis of positron emission tomography (PET) of mouse brains, deep learning (DL)-based unified solutions, particularly for spatial normalization (SN), have posed a challenging problem in DL-based image processing. In this study, we propose an approach based on DL to resolve these issues. We generated both skull-stripping masks and individual brain-specific volumes-of-interest (VOIs-cortex, hippocampus, striatum, thalamus, and cerebellum) based on inverse spatial normalization (iSN) and deep convolutional neural network (deep CNN) models. We applied the proposed methods to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans two times, before and after the administration of human immunoglobulin or antibody-based treatments. For training the CNN, manually traced brain masks and iSN-based target VOIs were used as the label. We compared our CNN-based VOIs with conventional (template-based) VOIs in terms of the correlation of standardized uptake value ratio (SUVR) by both methods and two-sample t-tests of SUVR % changes in target VOIs before and after treatment. Our deep CNN-based method successfully generated brain parenchyma mask and target VOIs, which shows no significant difference from conventional VOI methods in SUVR correlation analysis, thus establishing methods of template-based VOI without SN.

Distinctive Temporal Trajectories of Alzheimer's Disease Biomarkers According to Sex and APOE Genotype: Importance of Striatal Amyloid.

PURPOSE: Previously, sex and apolipoprotein E (APOE) genotype had distinct effects on the cognitive trajectory across the Alzheimer's disease (AD) continuum. We therefore aimed to investigate whether these trajectory curves including ß-amyloid (Aß) accumulation in the cortex and striatum, and tau accumulation would differ according to sex and APOE genotype. METHODS: We obtained 534 subjects for 18F-florbetapir (AV45) PET analysis and 163 subjects for 18F-flortaucipir (AV1451) PET analysis from the Alzheimer's Disease Neuroimaging Initiative database. For cortical Aß, striatal Aß, and tau SUVR, we fitted penalized splines to model the slopes of SUVR value as a non-linear function of baseline SUVR value. By integrating the fitted splines, we obtained the predicted SUVR curves as a function of time. RESULTS: The time from initial SUVR to the cutoff values were 14.9 years for cortical Aß, 18.2 years for striatal Aß, and 22.7 years for tau. Although there was no difference in cortical Aß accumulation rate between women and men, striatal Aß accumulation was found to be faster in women than in men, and this temporal difference according to sex was more pronounced in tau accumulation. However, APOE ε4 carriers showed faster progression than non-carriers regardless of kinds of AD biomarkers' trajectories. CONCLUSION: Our temporal trajectory models illustrate that there is a distinct progression pattern of AD biomarkers depending on sex and APOE genotype. In this regard, our models will be able to contribute to designing personalized treatment and prevention strategies for AD in clinical practice.

Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments.

We aimed to quantitatively and qualitatively assess whether there is a discrepancy in detecting amyloid beta (Aß) positivity between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) positron emission tomography (PET). We obtained paired FBB and FMM PET images from 107 participants. Three experts visually quantified the Aß deposition as positive or negative. Quantitative assessment was performed using global cortical standardized uptake value ratio (SUVR) with the whole cerebellum as the reference region. Inter-rater agreement was excellent for FBB and FMM. The concordance rates between FBB and FMM were 94.4% (101/107) for visual assessment and 98.1% (105/107) for SUVR cut-off categorization. Both FBB and FMM showed high agreement rates between visual assessment and SUVR positive or negative categorization (93.5% in FBB and 91.2% in FMM). When the two ligands were compared based on SUVR cut-off categorization as standard of truth, although not statistically significant, the false-positive rate was higher in FMM (9.1%) than in FBB (1.8%) (p = 0.13). Our findings suggested that both FBB and FMM had excellent agreement when used to quantitatively and qualitatively evaluate Aß deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers is feasible.

The Impact of Amyloid-ß or Tau on Cognitive Change in the Presence of Severe Cerebrovascular Disease.

BACKGROUND: As Alzheimer's disease (AD) and cerebral small vessel disease (CSVD) commonly coexist, the interaction between two has been of the considerable interest. OBJECTIVE: We determined whether the association of Aß and tau with cognitive decline differs by the presence of significant CSVD. METHODS: We included 60 subcortical vascular cognitive impairment (SVCI) from Samsung Medical Center and 82 Alzheimer's disease-related cognitive impairment (ADCI) from ADNI, who underwent Aß (florbetaben or florbetapir) and tau (flortaucipir, FTP) PET imaging. They were retrospectively assessed for 5.0±3.9 and 5.6±1.9 years with Clinical Dementia Rating-sum of boxes (CDR-SB)/Mini-Mental State Examination (MMSE). Mixed effects models were used to investigate the interaction between Aß/tau and group on CDR-SB/MMSE changes. RESULTS: The frequency of Aß positivity (45% versus 54.9%, p = 0.556) and mean global FTP SUVR (1.17±0.21 versus 1.16±0.17, p = 0.702) were not different between the two groups. We found a significant interaction effect of Aß positivity and SVCI group on CDR-SB increase/MMSE decrease (p = 0.013/p < 0.001), and a significant interaction effect of global FTP uptake and SVCI group on CDR-SB increase/MMSE decrease (p < 0.001 and p = 0.030). Finally, the interaction effects of regional tau and group were prominent in the Braak III/IV (p = 0.001) and V/VI (p = 0.003) not in Braak I/II region (p = 0.398). CONCLUSION: The association between Aß/tau and cognitive decline is stronger in SVCI than in ADCI. Therefore, our findings suggested that Aß positivity or tau burden (particularly in the Braak III/IV or V/VI regions) and CSVD might synergistically affect cognitive decline.

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clinically relevant issue is the selection of high-risk subjects who need active surveillance among equivocal cases. We validated the clinical feasibility of DL compared with visual rating or quantitative measurement for assessing the diagnosis and prognosis of subjects with equivocal amyloid scans. METHODS: 18F-florbetaben scans of 430 cases (85 NC, 233 mild cognitive impairment, and 112 AD) were assessed through visual rating-based, quantification-based, and DL-based methods. DL was trained using 280 two-dimensional PET images (80%) and tested by randomly assigning the remaining (70 cases, 20%) cases and a clinical validation set of 54 equivocal cases. In the equivocal cases, we assessed the agreement among the visual rating, quantification, and DL and compared the clinical outcome according to each modality-based amyloid status. RESULTS: The visual reading was positive in 175 cases, equivocal in 54 cases, and negative in 201 cases. The composite SUVR cutoff value was 1.32 (AUC 0.99). The subject-level performance of DL using the test set was 100%. Among the 54 equivocal cases, 37 cases were classified as positive (Eq(deep+)) by DL, 40 cases were classified by a second-round visual assessment, and 40 cases were classified by quantification. The DL- and quantification-based classifications showed good agreement (83%, κ = 0.59). The composite SUVRs differed between Eq(deep+) (1.47 [0.13]) and Eq(deep-) (1.29 [0.10]; P < 0.001). DL, but not the visual rating, showed a significant difference in the Mini-Mental Status Examination score change during the follow-up between Eq(deep+) (- 4.21 [0.57]) and Eq(deep-) (- 1.74 [0.76]; P = 0.023) (mean duration, 1.76 years). CONCLUSIONS: In visually equivocal scans, DL was more related to quantification than to visual assessment, and the negative cases selected by DL showed no decline in cognitive outcome. DL is useful for clinical diagnosis and prognosis assessment in subjects with visually equivocal amyloid scans.

Bilirubin-Related Differential Striatal [18F]FP-CIT Uptake in Parkinson Disease.

PURPOSE OF THE REPORT: Oxidative stress is a leading factor in the pathogenesis of idiopathic Parkinson disease (IPD). Two intrinsic antioxidative molecules, bilirubin and uric acid, are known to protect dopaminergic neurons from oxidative stress in IPD. The objective of this study was to determine the relationship between basal serum levels of 2 molecules and dopaminergic deficit assessed by dopamine transporter imaging with F-fluorinated-N-3-fluoropropyl-2-ß-carboxymethoxy-3-ß-(4-iodophenyl)nortropane ([F]FP-CIT) PET/CT in patients with early-stage drug-naive IPD. METHODS: Cases of IPD patients who possess the levels of uric acid and bilirubin within a month from [F]FP-CIT PET/CT from January 2011 to December 2016 were retrospectively reviewed. As a control, the same criteria applied to patients with essential tremor (ET). PET images were analyzed using volume-of-interest templates for 12 striatal subregions and 1 occipital area, and the specific-to-nonspecific binding ratio (SNBR) was calculated. RESULTS: One hundred five patients with drug-naive, early-stage IPD and 62 patients with ET were finally included. Levels of bilirubin were significantly higher in the IPD group than in controls (P = 0.026), and bilirubin level was the factor showing the most correlations with SNBR in IPD (P < 0.001), whereas uric acid showed no such difference or relationship. Furthermore, levels of bilirubin showed a positive correlation with SNBR in more affected posterior putamen in the IPD group (Pearson correlation coefficient, ρ = 0.456; P < 0.001), but a negative one in the ET group (ρ = -0.440, P < 0.001). CONCLUSIONS: Bilirubin, not uric acid, was the most significant antioxidant marker for dopaminergic deficit in early-stage drug-naive IPD assessed by [F]FP-CIT PET/CT.

Lack of association between dopamine transporter loss and non-motor symptoms in patients with Parkinson's disease: a detailed PET analysis of 12 striatal subregions.

INTRODUCTION: Patients with Parkinson's disease (PD) present a variety of non-motor symptoms. However, it remains unclear whether dopamine depletion is related to non-motor symptoms, and which non-motor symptoms are significantly dependent on dopaminergic deficit. METHODS: Forty-one patients with PD who underwent positron emission tomography imaging of dopamine transporters (DATs) were recruited for this study. The striatum was divided into 12 subregions, and DAT activity, as striatal dopaminergic concentration, was calculated in each subregion. In addition to measuring motor symptoms using the Unified Parkinson's Disease Rating Scale-part III (UPDRS-III), various non-motor symptoms were assessed using the Montreal cognitive assessment, frontal assessment battery, Beck depression inventory (BDI), Beck anxiety inventory, PD sleep scale (PDSS), PD fatigue scale, and non-motor symptoms scale (NMSS) for PD. RESULTS: For simple linear regression analyses, dopaminergic depletion in all striatal subregions was negatively correlated with the UPDRS-III score. The most relevant non-motor symptom assessment related to dopaminergic loss in the 12 subregions was NMSS, followed by BDI and PDSS. However, following multiple linear regression analyses, dopaminergic depletion in the 12 striatal subregions was not related with any of the non-motor symptoms. Conversely, dopaminergic deficit in the right anterior and posterior putamen was associated with the UPDRS-III score. CONCLUSIONS: Striatal dopaminergic depletion was not significantly correlated with any of the various non-motor symptoms in PD. Our findings suggest that non-dopaminergic systems are significantly implicated in the pathogenesis of non-motor symptoms in patients with PD.

Pre-treatment metabolic tumor volume predicts tumor metastasis and progression in high-grade salivary gland carcinoma.

PURPOSE: High-grade salivary gland carcinoma (SGC) shows a high rate of metastasis and post-treatment recurrence, resulting in poor patient survival. Therefore, we evaluated the utility of pre-treatment 18F-FDG PET/CT parameters in predicting metastasis, tumor progression, and survival of high-grade SGC patients. METHODS: This observational study included 75 patients with previously untreated high-grade SGC who underwent pre-treatment 18F-FDG PET/CT scanning and subsequent treatment. Standardized uptake values (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured on pre-treatment 18F-FDG PET/CT. Logistic regression analysis was used to identify the relationship of these factors with metastasis. Cox proportional hazard regression analyses were used to identify associations between PET parameters and both progression-free survival (PFS) and overall survival (OS). RESULTS: A total of 36 (48%) patients had neck or distant metastases at initial staging. After controlling for clinical factors, MTV (> 8.8 mL) was an independent factor for initial metastasis (adjusted odds ratio 4.80, 95% confidence interval 1.09-21.20; P = 0.039). All PET parameters of SUVmax (> 4.3), SUVmean (> 3.0), SUVpeak (3.9), MTV (> 8.8 mL), and TLG (> 31.1 g) were significant variables for PFS (all P < 0.05), while MTV and TLG were significant factors for OS. After controlling for clinicopathological factors, MTV (adjusted hazard ratio 4.36, 95% confidence interval 1.69-11.26; P = 0.002) and TLG (3.41, 1.47-7.91; P = 0.004) were significantly associated with PFS, but not OS. CONCLUSIONS: MTV is useful among quantitative PET measurements for predicting initial metastasis and PFS in patients with high-grade SGC.

Prognostic value of 18 F-FDG PET/CT parameters in patients who undergo salvage treatments for recurrent squamous cell carcinoma of the larynx and hypopharynx.

BACKGROUND: Recurrent laryngeal/hypopharyngeal squamous cell carcinoma (LHSCC) is commonly associated with poor survival outcomes. We evaluated the prognostic role of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (18 F-FDG PET/CT) parameters quantitatively measured in patients who underwent salvage treatments for recurrent LHSCC. METHODS: This study involved 100 consecutive LHSCC patients who underwent 18 F-FDG PET/CT for recurrent staging and subsequent salvage treatments. Maximum standardized uptake value (SUVmax ), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured using 18 F-FDG PET/CT. Cox proportional hazards regression analyses were used to assess the associations between quantitative 18 F-FDG PET/CT parameters and other clinicopathological factors with progression-free survival (PFS) and overall survival (OS). RESULTS: Two-year postsalvage PFS and OS rates were 67.9% and 74.3%, respectively. All 18 F-FDG PET parameters of SUVmax , MTV, and TLG were significantly associated with poor PFS and OS outcomes after salvage treatment (all P < 0.05). Multivariate analyses revealed that recurrence site, MTV (>6.5 mL), and TLG (>17.1 g) were independent variables predictive of PFS. Karnofsky score, SUVmax (>4.0), and TLG (>17.1 g) were the independent prognostic factors for OS. CONCLUSIONS: 18 F-FDG PET/CT can be useful in predicting postsalvage recurrence and survival in patients with recurrent LHSCC.

Is 18F-FDG PET/CT useful for the differential diagnosis of solitary pulmonary nodules in patients with idiopathic pulmonary fibrosis?

OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is associated with an increased incidence of lung cancer, but patients with IPF often have poor pulmonary function and are vulnerable to pneumothorax and so using an invasive procedure to diagnose a single nodule detected on chest CT risks a critical adverse outcome. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is recognized to be useful for differentiating between benign and malignant solitary pulmonary nodules (SPN) in patients without IPF, but its diagnostic accuracy has not been investigated in patients with IPF. In this study, therefore, we investigated whether 18F-FDG PET/CT is useful for the differential diagnosis of SPNs in patients with IPF. METHODS: From the IPF patient cohort of our institution, we retrospectively reviewed 55 patients (54 men, 1 woman; age 67.8 ± 7.6 years) with an SPN sized 8-30 mm (mean 18.5 ± 5.7 mm) who underwent chest CT followed by 18F-FDG PET/CT between April 2004 and March 2016. The 18F-FDG uptake of the SPN was analyzed visually and semiquantitatively, and these determinations were compared with the final diagnosis obtained by pathology (n = 52) or imaging follow-up (n = 3). RESULTS: The final diagnoses showed that 41 (75%) of the SPNs were malignant (21 squamous cell carcinomas, 9 adenocarcinomas, 5 small-cell carcinomas, 4 mixed-type carcinomas, 1 large-cell neuroendocrine carcinoma, and 1 sarcoid carcinoma) and 14 (25%) were benign. The determination of malignant SPNs by visual analysis of the PET/CT images had a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 98, 86, 95, and 92%, respectively. The semiquantitative analysis using a maximum standardized uptake value of 2.0 as the cut-off had a sensitivity, specificity, PPV, and NPV of 95, 93, 98, and 87%, respectively. CONCLUSIONS: 18F-FDG PET/CT is useful for differentiating benign and malignant SPNs in patients with IPF, as it is for patients without IPF.