18F-fluorodeoxyglucose Positron Emission Tomography Imaging Research Articles

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases.

BackgroundThe effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.MethodsOne hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC).ResultsThrough the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57–0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness.ConclusionsWe developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

Multimodal functional imaging for early response assessment in patients with gastrointestinal stromal tumor treated with tyrosine kinase inhibitors.

Several imaging modalities are used in the early work-up of patients with gastrointestinal stromal tumor (GIST) receiving tyrosine kinase inhibitor (TKI) treatment and there is a need to establish whether they provide similar or complimentary information. To compare 18F-fluorodeoxyglucose positron emission tomography (FDG PET), computed tomography (CT) and magnetic resonance imaging (MRI) as early predictors of three-month outcomes for patients with GIST receiving TKI treatment. Thirty-five patients with advanced GIST were prospectively included between February 2011 and June 2017. FDG PET, contrast-enhanced CT (CECT), and MRI were performed before and early after onset of TKI treatment (range 8-18 days). Early response was categorized according to mRECIST (CT), the Choi criteria (CECT), and PERCIST (FDG PET/CT). For MRI, volumetry from T2-weighted images and change in apparent diffusion coefficient (ADC) from diffusion-weighted imaging was used. The reference standard for early assessment was the three-month mRECIST evaluation based on CT. At three months, both stable disease (SD) and partial response (PR) were categorized as response. Clinical usefulness was defined as agreement between early and three-month assessment. At the three-month assessment, 91% (32/35) were responders, 37% (13/35) PR, 54% (19/35) SD, and 9% (3/35) had progressive disease (PD). Early assessment correctly predicted three-month response in 93% (27/29) for MRI, 80% (28/35) for PERCIST, 74% (26/35) for Choi, and 23% (8/35) for mRECIST. Six patients had non-FDG-avid tumors. For the FDG-avid tumors, PET/CT correctly predicted three-month response in 97% (28/29). MRI was superior to CECT for early assessment of TKI-treatment response in GIST. If the tumor was FDG-avid, PET and MRI were equally good. Changes in functional parameters were superior to changes in longest tumor diameter (mRECIST).

Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes.

Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using 18F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) imaging, and explore whether these differences can help distinguish. Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [18F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features. The |AI| was found significantly higher in atypical BECTS than in typical BECTS (p = 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70, p = 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models. Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS. • Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice. • Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex. • The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.

Effects of baseline serum uric acid and apolipoprotein E4 on longitudinal cognition and cerebral metabolism

Serum uric acid, a natural antioxidant, may have a protective effect on the progression of Alzheimer's disease (AD). To investigate the effect of serum uric acid on longitudinal cognitive and brain metabolic changes, we utilized data on baseline serum uric acid levels, APOE genotyping, and longitudinal cognitive scores from the Alzheimer's Disease Neuroimaging Initiative for 1,343 participants with normal cognition (NC), mild cognitive impairment (MCI), or dementia. In 979 participants, brain metabolism was measured using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images. Higher serum uric acid levels exhibited a detrimental effect on NC, whereas a protective trend was observed in individuals with cognitive impairment. Interestingly, higher uric acid levels were associated with a slower decline in cognitive scores and brain metabolism in females with MCI, and this effect was found in APOE4 carriers, but not in non-carriers. Longitudinal AD-like patterns of brain metabolism on FDG-PET images also appeared to mediate the effects of baseline uric acid levels on longitudinal cognitive decline. In summary, higher serum uric acid may interact with APOE4 to alleviate longitudinal metabolic changes and cognitive decline in female MCI patients.

Association of hypometabolic extension of 18F-FDG PET with diffusion tensor imaging indices in mesial temporal lobe epilepsy with hippocampal sclerosis

Purpose: To assess the association between hypometabolic extension of 18F-fluorodeoxyglucose positron emission tomography and diffusion tensor imaging indices, including mean diffusivity (MD) and fractional anisotropy (FA), in hippocampal sclerosis (HS). Methods: Thirty-six unilateral HS were retrospectively selected and stratified into two groups: broad and localized hypometabolic groups (hypometabolism beyond [n = 26] and within the temporal lobe [n = 10]). Forty-one pairs of gray matter (GM) regions of interest (ROIs) were segmented using FreeSurfer software. The GM ROIs were applied to MD maps, and median MD values within each ROI were compared between hemispheres ipsilateral and contralateral to HS using a mixed effect model. Tract-Based Spatial Statistics (TBSS) was used to evaluate FA of white matter (WM) tracts between hemispheres ipsilateral and contralateral to HS. Disease laterality was controlled for. Results: The MD values in the thalamus, caudate, hippocampus, amygdala, superior frontal gyrus, middle and inferior temporal gyrus, temporal pole, and isthmus cingulate gyrus were significantly higher in the HS side than the contralateral side for the broad hypometabolic group. Those in the amygdala and superior temporal sulcus were significantly higher in the HS side than the contralateral side for the localized group. The TBSS analyses showed significantly decreased FA in the WM tracts of the temporal and frontal lobes for the broad hypometabolic group, while no tracts showed significant differences for the localized group. Conclusion: The hypometabolic extension for HS was associated with the abnormalities of MD and FA in GM and WM, respectively, with more widespread microstructural alterations for broad hypometabolic HS.

T2-weighted, apparent diffusion coefficient and 18F-FDG PET histogram analysis of rectal cancer after preoperative chemoradiotherapy

BackgroundThe aim of our study was to investigate the correlation among T2-weighted (T2w) images, apparent diffusion coefficient (ADC) maps, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images, histogram analysis and the pathological response in locally advanced rectal cancer (LARC) after preoperative chemoradiotherapy (pCRT).MethodsPatients with LARC were prospectively enrolled between February 2015 and August 2018 and underwent PET/magnetic resonance imaging (MRI). MRI included T2w and diffusion-weighted imaging (DWI)-sequences. ADC maps and PET images were matched to the T2w images. Voxel-based standardized uptake values (SUVs,) ADC and T2w-signal-intensity values were collected from the volumes of interest (VOIs) and mean, skewness and kurtosis were calculated. Spearman’s correlation coefficient was applied to evaluate the correlation among the variables and tumor regression grade (TRG), T stage, N stage and fibrosis.ResultsTwenty-two patients with biopsy-proven LARC in the low or mid rectum were enrolled [17 males, mean age was 69 years (range 49–85 years)]. Seven patients experienced complete regression (TRG1). A significant positive correlation was found between SUV mean values (ρ = 0.480; p = 0.037) and TRG. No other significant correlations were found.ConclusionsHistogram analysis of SUV values is a predictor of TRG in LARC.

FDG PET imaging of vascular inflammation in post-traumatic stress disorder: A pilot case–control study

The prevalence of cardiovascular diseases (CVD) is increased in subjects with post-traumatic stress disorder (PTSD). Vascular inflammation mediates CVD and may be assessed by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging. In this pilot study, we investigated whether subjects with PTSD have enhanced vascular and systemic inflammation compared to healthy controls, as assessed by FDG PET imaging. MethodsA prospective group of 16 subjects (9 PTSD and 7 controls, age 34 ± 7) without prior history of CVD underwent FDG PET/CT imaging. The presence of PTSD symptoms at the time of the study was confirmed using PTSD checklist for DSM-5 (PCL5) questionnaire. Blood samples were collected to determine blood glucose, lipid and inflammatory biomarkers (tumor necrosis factor α, interleukin-1β, and interleukin-6) levels. FDG signal in the ascending aorta, amygdala, spleen and bone marrow was quantified. ResultsThe two groups matched closely with regards to cardiovascular risk factors. The inflammatory biomarkers were all within the normal range. There was no significant difference in FDG signal in the aorta (target to background ratio: 2.40 ± 0.29 and 2.34 ± 0.29 for control and PTSD subjects, difference: − 0.06, 95% confidence interval of difference: − 0.38 to 0.26), spleen, bone marrow, or amygdala between control and PTSD subjects. There was no significant correlation between aortic and amygdala FDG signal. However, a significant positive correlation existed between amygdala, splenic, and bone marrow FDG signal. ConclusionThis pilot, small study did not reveal any difference in vascular or systemic inflammation as assessed by FDG PET imaging between PTSD and healthy control subjects. Because of the small number of subjects, a modest increase in vascular inflammation, which requires larger scale studies to establish, cannot be excluded. The correlation between FDG signal in amygdala, spleen and bone marrow may reflect a link between amygdala activity and systemic inflammation.

Deep learning-based image quality improvement of 18F-fluorodeoxyglucose positron emission tomography: a retrospective observational study

BackgroundDeep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images obtained with the DL method with those obtained using a Gaussian filter.MethodsFifty patients with a mean age of 64.4 (range, 19–88) years who underwent 18F-FDG PET/CT between April 2019 and May 2019 were included in the study. PET images were obtained with the DL method in addition to conventional images reconstructed with three-dimensional time of flight-ordered subset expectation maximization and filtered with a Gaussian filter as a baseline for comparison. The reconstructed images were reviewed by two nuclear medicine physicians and scored from 1 (poor) to 5 (excellent) for tumor delineation, overall image quality, and image noise. For the semi-quantitative analysis, standardized uptake values in tumors and healthy tissues were compared between images obtained using the DL method and those obtained with a Gaussian filter.ResultsImages acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss’ kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001).ConclusionsDeep learning method improves the quality of PET images.

Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

Dementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

The Relationship Among Glucose Metabolism, Cerebral Blood Flow, and Functional Activity: a Hybrid PET/fMRI Study.

18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) estimate brain activities from different aspects, including regional glucose uptake (rGU) by 18FDG-PET, regional cerebral blood flow (rCBF) by arterial spin labeling, and dynamic changes of deoxyhemoglobin by blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). However, the relationships between them remain incompletely understood. In the current study, twenty-four subjects (14 males, 10 females) were recruited and investigated the correlation among rGU, rCBF, and BOLD fMRI-derived metrics reflecting the neural activity, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) by hybrid PET/fMRI. Correlation analyses were performed across subject and across space at both voxel level and region level, considering partial volume effects by adjusting for gray matter volume. Each pair of metrics showed significant across-space correlations. rGU against ReHo showed the highest mean correlation coefficients. rGU had higher correlations with three resting-state (RS) fMRI metrics than did ASL-rCBF. However, the across-subject correlations were not significant among functional modalities (rGU, rCBF, and RS-fMRI BOLD data) at either voxel level or region level even with a liberal threshold, except for significant across-subject correlation between RS-fMRI metrics (ALFF, ReHo, and DC). These comprehensive findings from hybrid PET/MR might provide complementary information to reveal the underlying mechanisms of the brain activity and open new perspective to interpret pathologic conditions.

A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy

PurposeEpilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE).MethodsWe retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis.ResultsThe proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31–0.44, P = 0.005–0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01).ConclusionThe proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients.Trial registrationNCT04169581. Registered November 13, 2019Public site: https://clinicaltrials.gov/ct2/show/NCT04169581

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

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 numberChiCTR2000029115 (Chinese clinical trial registry site, http://www.chictr.org)

Texture Analysis Using Preoperative Positron Emission Tomography Images May Predict the Prognosis of Patients With Resectable Oral Squamous Cell Carcinoma

Texture analysis is a computer-assisted technique used to measure intratumoral heterogeneity, which is known to have important roles in cancer research. This study aimed to assess the potential prognostic values of textural features extracted from preoperative 18F-fluorodeoxyglucose positron emission tomography images in patients with resectable oral squamous cell carcinoma. This retrospective cohort study included patients with oral squamous cell carcinoma who underwent resection surgery. We extracted 31 textural indices from preoperative positron emission tomography images. Overall survival (OS) and disease-free survival (DFS) were chosen as the primary outcome variables, and the primary predictor variables were age, sex, primary tumor location, pathological T and N classification, histologic differentiation, resected margin, perineural and lymphovascular invasion, maximum standardized uptake value, and the 14 textural indices selected in the factor analysis. We analyzed OS and DFS using Kaplan-Meier curves, and the differences between survival curves were determined using a log-rank test. The independent prognostic factors were assessed using the Cox-proportional hazards model. We enrolled 81 patients (median age, 67.3years; range, 32 to 88years). The median follow-up duration was 50.1months (range, 6.3 to 133.7months). The univariable and multivariable analyses revealed that higher entropy values (≥1.91) were associated with worse OS (hazard ratio, 21.49; 95% confidence interval, 1.36 to 340.71; P=.03) and DFS (hazard ratio, 50.69; 95% confidence interval, 5.23 to 491.18; P=.001). This study showed that entropy is a statistically significant prognostic factor of both OS and DFS. Texture analysis using preoperative positron emission tomography images may contribute to risk stratification.

Reading prosody in the non-fluent and logopenic variants of primary progressive aphasia

BackgroundPrimary progressive aphasia (PPA) is a clinical syndrome including a group of neurodegenerative disorders that present with language impairment. We hypothesised that impairment in reading prosody may be present in a subgroup of patients with PPA, and particularly non-fluent PPA (nfvPPA), because of the impairment of key brain regions involved in the pathophysiology of speech dysprosody and reading observed in these patients. MethodsNinety-five participants were evaluated using a narrative text comprising several declarative, exclamatory, and interrogative sentences, as well as a comprehensive language protocol. Patients were also examined with 18F-fluorodeoxyglucose positron emission tomography imaging. ResultsImpairment was more frequent and more severe in patients with nfvPPA, especially in the subgroup of patients with Apraxia of Speech (AOS). Patients with nfvPPA, mainly those with AOS, showed lower values in several pitch variables. Statistically significant differences were also observed in sentence duration, reading times, and types of error. A regression model including mean length of utterances, time after full stops, number of pauses, and number of pitch variables below the mean, correctly classified 70–71.3% of patients. When combined with sentence repetition task, the percentage of patients correctly classified was 96.2% and 92.4%, respectively, for each classification. The left frontal lobe was the region most strongly correlated with reading prosody parameters. Specific tasks displayed additional correlations with the left parietal and occipital lobes; right frontal lobe, thalamus, and caudate; and right cerebellum. ConclusionReading prosody is relevant in PPA diagnosis and classification. Because reading prosody may be quantified, it is amenable to use in diagnosis and follow-up. We found neuroimaging correlation with metabolism in the left frontal lobe, as well as in other regions including the right frontal lobe, basal ganglia, and cerebellum, which suggests that these may be the main brain regions involved in prosodic control in patients with PPA.

Gout and pseudo-gout-related crystals promote GLUT1-mediated glycolysis that governs NLRP3 and interleukin-1β activation on macrophages

ObjectiveMacrophage activation by monosodium urate (MSU) and calcium pyrophosphate (CPP) crystals mediates an interleukin (IL)-1β-dependent inflammation during gout and pseudo-gout flare, respectively. Since metabolic reprogramming of macrophages goes along with...

Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Background/aimIn temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe. Materials and methods Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians’ visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size. Results In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold cross-validation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm.Conclusion Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values ​​obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

Brain Functional and Structural Signatures in Parkinson's Disease.

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Multiparametric positron emission tomography/magnetic resonance imaging in nasopharyngeal carcinoma: Correlations between magnetic resonance imaging functional parameters and 18F-fluorodeoxyglucose positron emission tomography imaging biomarkers and their predictive value for treatment failure.

Objectives:The clinical significance of positron emission tomography/magnetic resonance imaging (PET/MRI) functional parameters in nasopharyngealcarcinoma (NPC) remains unclear. The purpose of this prospective study was two-fold: (1) to investigate the associations between simultaneously acquired PET/MRI perfusion, diffusion, and glucose metabolism parameters in patients with NPC and (2) to analyze their predictive value with respect to treatment failure.Materials and Methods:We enrolled 85 patients with primary NPC who simultaneously underwent18F-fluorodeoxyglucose PET/CT and PET/MRI before definitive treatment. The following variables were determined: (1) functional parameters from the MRI component, including perfusion values (Ktrans,kep,ve, and initial area under the enhancement curve) and apparent diffusion coefficient (ADC) values, and (2) PET parameters, including metabolic tumor volume (MTV). The reciprocal interrelationships between these parameters and their correlations with treatment failure were examined.Results:We observed significant negative associations between Ktrans and ADC (r = −0.215, P = 0.049) as well as between ve and ADC (r = −0.22, P = 0.04). Correlations between PET and MRI functional parameters were not statistically significant. Treatment failures were observed in 21.2% of patients without distant metastases. Multivariate analysis identified ve as a significant independent predictor for treatment failure (P = 0.022), whereas MTV showed a borderline significance (P = 0.095). Patients in whom both ve and MTV values were increased had a significantly higher rate of treatment failure (62.5%) than those with either one (21.9%) or no (7.7%) increased parameter (P = 0.004).Conclusion:Correlation analyses revealed complex interrelationships among PET and MRI indices measured in patients with NPC. These parameters may have a complementary role in predicting treatment failure in this clinical setting.

Resting State Glucose Utilization and Adult Reading Test Performance.

Adult reading tests (ART) have been widely used in both research and clinical settings as a measure of premorbid cognitive abilities or cognitive reserve. However, the neural substrates underlying ART performance are largely unknown. Furthermore, it has not yet been examined whether the neural substrates of ART performance reflect the cortical regions associated with premorbid intelligence or cognitive reserve. The aim of the study is to identify the functional neural correlates of ART performance using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging in the cognitively normal (CN) middle- and old-aged adults. Voxel-wise analyses revealed positive correlations between glucose metabolism and ART performance in the frontal and primary somatosensory regions, more specifically the lateral frontal cortex, anterior cingulate cortex and postcentral gyrus (PCG). When conducted again only for amyloid-β (Aβ)-negative individuals, the voxel-wise analysis showed significant correlations in broader areas of the frontal and primary somatosensory regions. This is the first neuroimaging study to directly demonstrate the cerebral resting-state glucose utilization associated with ART performance. Our findings provide important evidence at the neural level that ART predicts premorbid general intelligence and cognitive reserve, as brain areas that showed significant correlations with ART performance correspond to regions that have been associated with general intelligence and cognitive reserve.

Epileptogenic zone localization using a new automatic quantitative analysis based on normal brain glucose metabolism database

Objectives To assess the clinical value of voxel-based automatic quantitative analysis using a normal brain glucose metabolism database in the preoperative localization of focal intractable temporal lobe epilepsy patients. Methods Patients with refractory temporal lobe epilepsy who underwent 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging were retrospectively enrolled from January to June 2017. Visual analysis was performed by two nuclear medicine radiologists, and the automatic quantitative analysis was carried out using MIMneuro software based the age- and gender-stratified normal brain glucose metabolism database. Setting postoperative outcomes as reference, the consistency between visual analysis and automatic quantitative analysis was tested by Cohen’s kappa coefficient, and differences in localization of epileptic foci of the two methods were compared by Chi-square test. Results A total of 32 patients intractable temporal lobe epilepsy were included in this study. There was a moderate agreement between the automatic quantitative analysis based on MIMneuro software and visual analysis (kappa coefficient = 0.472, p = 0.002). In terms of the efficiency of focus localization, the voxel-based automatic quantitative analysis was higher than that of visual analysis (Chi-square value = 6.969, p = 0.008). Conclusions The voxel-based automatic quantitative analysis combined with normal brain glucose metabolism database had a certain clinical application value for detection temporal lobe epilepsy.