18F-FDG PET Scans Research Articles

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease's progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits.

A Rare Axillary Lymph Node Metastasis on 18F-FDG PET/CT for Staging in a Patient with Common Bile Duct Cancer.

Distant metastasis of cholangiocarcinoma is most commonly diagnosed in the liver; however, it can also be found in the lungs, distant lymph nodes, bones, and brain. Distant lymph node metastasis outside the abdominal region without concurrent abdominal metastasis is exceedingly rare in extrahepatic cholangiocarcinoma. Herein, we present interesting 18F-FDG PET/CT images of a 49-year-old male patient with common bile duct cancer. In this case, the patient, who was scheduled for surgery, unexpectedly showed axillary lymph node metastasis on a preoperative 18F-FDG PET scan, which was subsequently confirmed via histological examination. Although such cases are exceptionally rare, this accurate diagnosis prompted a modification of the treatment plan, leading to a positive therapeutic response.

Framing protocol optimization in oncological Patlak parametric imaging with uKinetics

PurposeTotal-body PET imaging with ultra-high sensitivity makes high-temporal-resolution framing protocols possible for the first time, which allows to capture rapid tracer dynamic changes. However, whether protocols with higher number of temporal frames can justify the efficacy with substantially added computation burden for clinical application remains unclear. We have developed a kinetic modeling software package (uKinetics) with the advantage of practical, fast, and automatic workflow for dynamic total-body studies. The aim of this work is to verify the uKinetics with PMOD and to perform framing protocol optimization for the oncological Patlak parametric imaging.MethodsSix different protocols with 100, 61, 48, 29, 19 and 12 temporal frames were applied to analyze 60-min dynamic 18F-FDG PET scans of 10 patients, respectively. Voxel-based Patlak analysis coupled with automatically extracted image-derived input function was applied to generate parametric images. Normal tissues and lesions were segmented manually or automatically to perform correlation analysis and Bland–Altman plots. Different protocols were compared with the protocol of 100 frames as reference.ResultsMinor differences were found between uKinetics and PMOD in the Patlak parametric imaging. Compared with the protocol with 100 frames, the relative difference of the input function and quantitative kinetic parameters remained low for protocols with at least 29 frames, but increased for the protocols with 19 and 12 frames. Significant difference of lesion Ki values was found between the protocols with 100 frames and 12 frames.ConclusionuKinetics was proved providing equivalent oncological Patlak parametric imaging comparing to PMOD. Minor differences were found between protocols with 100 and 29 frames, which indicated that 29-frame protocol is sufficient and efficient for the oncological 18F-FDG Patlak applications, and the protocols with more frames are not needed. The protocol with 19 frames yielded acceptable results, while that with 12 frames is not recommended.

Diagnostic Superiority of 18 F-FDG PET Over MRI in Detecting Anti-LGI1 Autoimmune Encephalitis : A Comparative Study With Insights Into Faciobrachial Dystonic Seizures Mechanisms and Recurrence Identification.

Our study aimed to investigate the utility of 18 F-FDG PET imaging in diagnosing and monitoring patients with anti-leucine-rich glioma-inactivated 1 antibody autoimmune encephalitis (anti-LGI1 AE). We also sought to understand the mechanisms of faciobrachial dystonic seizures (FBDSs). We analyzed 18 F-FDG PET scans from 50 patients with anti-LGI1 AE, using visual and semiquantitative methods, and compared these with 24 healthy controls. All patients tested positive for anti-LGI1 antibodies in serum or cerebrospinal fluid before PET imaging. The patients were divided into FBDS and non-FBDS groups to compare metabolic differences using voxel-based semiquantitative analysis. Finally, we separately analyzed PET images of patients with symptom recurrence. The sensitivity of 18 F-FDG PET was superior to MRI (97.9% vs 63.8%, respectively; P < 0.001). Semiquantitative analysis revealed hypermetabolism in the basal ganglia, medial temporal lobe, and brainstem, and hypometabolism in most neocortical regions compared with healthy controls. The FBDS group exhibited hypometabolism in the frontal and temporal lobes compared with the non-FBDS group. Among 7 recurrent patients, 3 were confirmed as recurrence and 3 as sequelae by PET. One patient relapsed shortly after discontinuing corticosteroids when PET indicated active lesions. 18 F-FDG PET scans were more sensitive than MRI in detecting anti-LGI1 AE, which displayed a pattern of hypermetabolism in the basal ganglia and medial temporal lobe, as well as neocortex hypometabolism. Hypometabolism in the frontal and temporal lobes was associated with FBDS. Furthermore, 18 F-FDG PET scans can differentiate recurrence from sequelae and guide the timing of immunotherapy cessation.

Occipital hypometabolism is a risk factor for conversion to Parkinson’s disease in isolated REM sleep behaviour disorder

PurposeIsolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion.MethodsTwenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson’s disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated.ResultsIndividual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism.ConclusionsOur results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.

Correlation analysis between 18F-fluorodeoxyglucose positron emission tomography and cognitive function in first diagnosed Parkinson's disease patients.

Evaluation of the correlation between 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET) and cognitive function in first-diagnosed and untreated Parkinson's disease (PD) patients. This cross-sectional study included 84 first diagnosed and untreated PD patients. The individuals were diagnosed by movement disorder experts based on the 2015 MDS Parkinson's disease diagnostic criteria. The patients also underwent 18F-FDG PET scans and clinical feature assessments including the Montreal Cognitive Assessment (MoCA) scale. Glucose metabolism rates were measured in 26 brain regions using region of interest (ROI) and pixel-wise analyses with displayed Z scores. The cognitive function was assessed by professionals using the MoCA scale, which covers five cognitive domains. Spearman's linear correlation and linear regression models were used to compare the correlations between 18F-FDG metabolism in each brain region and cognitive domain, using SPSS 25.0 software. The results indicated a positive correlation between executive function and glucose metabolism in the lateral prefrontal cortex of the left hemisphere (p = 0.041). Additionally, a positive correlation between memory function and glucose metabolism in the right precuneus (p = 0.014), right lateral occipital cortex (p = 0.017), left lateral occipital cortex (p = 0.031), left primary visual cortex (p = 0.008), and right medial temporal cortex (p = 0.046). Further regression analysis showed that for every one-point decrease in the memory score, the glucose metabolism in the right precuneus would decrease by 0.3 (B = 0.30, p = 0.005), the glucose metabolism in the left primary visual cortex would decrease by 0.25 (B = 0.25, p = 0.040), the glucose metabolism in the right lateral occipital cortex would decrease by 0.38 (B = 0.38, p = 0.012), and the glucose metabolism in the left lateral occipital cortex would decrease by 0.32 (B = 0.32, p = 0.045). This study indicated that cognitive impairment in PD patients mainly manifests as changes in executive function, visual-spatial function and memory functions, while glucose metabolism mainly decreases in the frontal and posterior cortex. Further analysis shows that executive function is related to glucose metabolism in the left lateral prefrontal cortex. On the other hand, memory ability involves changes in glucose metabolism in a more extensive brain region. This suggests that cognitive function assessment can indirectly reflect the level of glucose metabolism in the relevant brain regions.

Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy.

To investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes. 18F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using 18F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model. The results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (PFDR = 0.0230, PFDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (PFDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (PFDR = 0.0152; PFDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914. These findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation.

Association of 18F-FDG PET characteristics and survival outcomes using whole body tumor analysis in patients (pts) with metastatic genitourinary (GU) malignancies.

4600 Background: 18F-FDG PET scans are widely used for staging and monitoring pts with metastatic GU malignancies. In addition to identifying lesions, 18F-FDG PET scans provide semi-quantitative parameters (SUVmax, SUVmean, total lesion glycolysis (TLG)) that can be used to characterize disease burden and treatment response. These parameters can demonstrate how treatment response varies across different patients as well as across different lesions within the same patient. This study was done to determine which parameter provides the best prognostic information for pts with metastatic GU malignancies. Methods: 101 patients with metastatic GU malignancies from two separate, prospective trials with at least two 18F-FDG PET scans were analyzed. All regions of interest were contoured for both the baseline and follow-up 18F-FDG PET scans. Regions of interest (ROI) were quantified and matched between timepoints using TRAQinform IQ technology (AIQ Solutions) to identify which were new, increasing, stable, decreasing, or disappeared. Imaging features were extracted from each patient, including basic features (SUVmax, SUVmean, TLG, and changes in these features) and heterogeneity (intrapatient heterogeneity of disease and response) features. Cox regression was used to determine univariate predictive power of each measure. The TRAQinform Profile was calculated to predict either progression-free (PFS) or overall survival (OS) using 5-fold cross-validation of a random survival forest. The performance of individual features and the TRAQinform Profile was evaluated using the c-index. Results: 54pts were treated with single agent cabozantinib (cabo) on a phase II trial and 47 pts were treated with cabozantinib and nivolumab, with or without ipilimumab (cabonivo +/- ipi), on a phase I trial. Most pts had urothelial carcinoma (N = 65). Other histologies included prostate (4), penile (1), renal cell carcinoma (RCC) (6), renal medullary carcinoma (4), testicular (4), adenocarcinoma of the bladder (8), squamous cell carcinoma of the bladder (2), sarcomatoid bladder (1), sarcomatoid RCC (1), and small cell carcinoma (4 bladder, 1 prostate). 2389 ROIs were analyzed, with a range of 1 to 151 per patient. The TLG at the follow-up image was the strongest predictor of both PFS (c-index = 0.62) and OS (0.69), followed by number of increasing ROI (0.61, 0.64), and percentage of all ROIs classified as new or increasing (0.57, 0.63). SUVmax (0.49, 0.47) and SUVmean (0.47, 0.56) at follow-up were found to be weaker predictors of PFS and OS. TRAQinform Profile was able to predict the responder’s vs suboptimal responders to study treatment (c-index = 0.73, 0.75). Conclusions: The TRAQinform Profile analysis of 18F-FDG PET scans provided both prognostic and predictive information for patients with metastatic GU malignancies treated with either cabo or cabonivo +/- ipi.

PET/MRI Assessment of Acute Cardiac Inflammation 1 Month After Left-Sided Breast Cancer Radiation Therapy.

Our purpose was to investigate the utility of 18F-FDG PET/MRI and serial blood work to detect early inflammatory responses and cardiac functionality changes at 1 mo after radiation therapy (RT) in patients with left-sided breast cancer. Methods: Fifteen left-sided breast cancer patients who enrolled in the RICT-BREAST study underwent cardiac PET/MRI at baseline and 1 mo after standard RT. Eleven patients received deep-inspiration breath-hold RT, whereas the others received free-breathing RT. A list-mode 18F-FDG PET scan with glucose suppression was acquired. Myocardial inflammation was quantified by the change in 18F-FDG SUVmean (based on body weight) and analyzed on the basis of the myocardial tissue associated with the left anterior descending, left circumflex, or right coronary artery territories. MRI assessments, including left ventricular functional and extracellular volumes (ECVs), were extracted from T1 (before and during a constant infusion of gadolinium) and cine images, respectively, acquired simultaneously during the PET acquisition. Cardiac injury and inflammation biomarker measurements of high-sensitivity troponin T, high-sensitivity C-reactive protein, and erythrocyte sedimentation rate were measured at the 1-mo follow-up and compared with preirradiation values. Results: At the 1-mo follow-up, a significant increase (10%) in myocardial SUVmean in left anterior descending segments (P = 0.04) and ECVs in slices at the apex (6%) and base (5%) was detected (P ≤ 0.02). Further, a significant reduction in left ventricular stroke volume (-7%) was seen (P < 0.02). No significant changes in any circulating biomarkers were seen at follow-up. Conclusion: Myocardial 18F-FDG uptake and functional MRI, including stroke volume and ECVs, were sensitive to changes at 1 mo after breast cancer RT, with findings suggesting an acute cardiac inflammatory response to RT.

Prediction of Treatment Failure in Hodgkin Lymphoma: A Machine Learning Radiomic Approach in Baseline 18F-FDG PET/CT

Purpose: Hodgkin Lymphoma staging and prognostic evaluation through radiomic and machine learning (ML) methods has been little explored. This study explores radiomic features and proposes a new ML radiomic model to predict treatment failure in Hodgkin lymphoma patients from PET images. Methods: 13 radiomic features were extracted from PET images of 51 subjects with Hodgkin’s Lymphoma with response to therapy and 6 subjects with treatment failure as classified by the Lugano criteria for lymphoma response. Univariate analysis was performed for feature selection by means of the areas under ROC curves (AUC), and Pearson correlation was assessed to reduce redundancy in the feature space. A ML algorithm was developed and trained-validated with a six-fold stratified cross validation. The ML performance was assessed through the results of 15 combinations of two-fold validation samples, deriving two ML methods (M1 and M2) for tumor classification at the tumor and patient levels. Results: The features zone percentage (ZP), high intensity large area emphasis (HILAE), entropy and standardized uptake value of maximum pixel (SUVmax) were independent predictors of treatment failure. The ML algorithm performed with AUC=0.86 (M1) and AUC=0.96 (M2) to classify “individual tumors” and at “patient level” performed with sensitivity = 80.0% /specificity = 88.3% (M1) and sensitivity = 100% /specificity = 100% (M2). The ML outperformed the Ann Arbor staging that achieved, respectively, 83.3% and 31.7% for sensitivity and specificity. Conclusions: Evaluation of baseline 18F-FDG PET scans of individuals with Hodgkin’s lymphoma using a ML radiomic model enabled accurate classification of patients at higher risk of treatment failure.

Thermogenic Capacity of Human Supraclavicular Brown Fat and Cold-Stimulated Brain Glucose Metabolism.

Human brain metabolism is susceptible to temperature changes. It has been suggested that the supraclavicular brown adipose tissue (BAT) protects the brain from these fluctuations by regulating heat production through the presence of uncoupling protein 1 (UCP-1). It remains unsolved whether inter-individual variation in the expression of UCP-1, which represents the thermogenic capacity of the supraclavicular BAT, is linked with brain metabolism during cold stress. Ten healthy human participants underwent 18F-FDG PET scanning of the brain under cold stimulus to determine brain glucose uptake (BGU). On a separate day, an excision biopsy of the supraclavicular fat-the fat proximal to the carotid arteries supplying the brain with warm blood-was performed to determine the mRNA expression of the thermogenic protein UCP-1. Expression of UCP-1 in supraclavicular BAT was directly related to the whole brain glucose uptake rate determined under cold stimulation (rho = 0.71, p = 0.03). In sub-compartmental brain analysis, UCP-1 expression in supraclavicular BAT was directly related to cold-stimulated glucose uptake rates in the hypothalamus, medulla, midbrain, limbic system, frontal lobe, occipital lobe, and parietal lobe (all rho ≥ 0.67, p < 0.05). These relationships were independent of body mass index and age. When analysing gene expressions of BAT secretome, we found a positive correlation between cold-stimulated BGU and DIO2. These findings provide evidence of functional links between brain metabolism under cold stimulation and UCP-1 and DIO2 expressions in BAT in humans. More research is needed to evaluate the importance of these findings in clinical outcomes, for instance, in examining the supporting role of BAT in cognitive functions under cold stress.

Computed Tomography-Based Texture Analysis as a Predictor of Survival in Patients with T-Cell Lymphomas

Background: T-cell lymphomas constitute a heterogeneous group of hematological malignancies with global poor outcomes. Computed tomography (CT)-based texture analysis (CTTA) is a promising technique to predict the survival of these patients. Objectives: The present study aimed to investigate whether CTTA features on the pretreatment unenhanced CT scans of 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) examination can predict the survival of patients with T-cell lymphomas. Patients and Methods: In this retrospective cohort study, patients with T-cell lymphomas, undergoing pretreatment 18F-FDG PET/CT scan during 2008 - 2019, were included, and their clinical and biological characteristics were collected. The mean gray value, entropy, kurtosis, skewness, and standard deviation were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales in up to five lesions per patient, indicating a high focal uptake on 18F-FDG PET scan. A Lasso-penalized Cox regression analysis was performed to identify independent predictors of overall survival (OS), progression-free survival at 24 months (PFS24), and PFS. Results: A total of 23 patients (7 females and 16 males; median age, 69 years; age range, 33 - 86 years) were included in this study. The CTTA was performed for 60 lesions. The median OS and PFS were 391 days (range, 10 - 3,463 days) and 268 days (range, 10 - 2,321 days), respectively. No CT texture parameter was associated with PFS or PFS24. The standard deviation at a coarse filter scale was independently associated with a poor OS (hazard ratio [HR] = 1.009, confidence interval [CI]: 1.0012 - 1.016, P = 0.02). A value above 143 was associated with a poor prognosis with a specificity of 81%. Conclusion: The pretreatment CTTA-derived tumor standard deviation at a coarse filter scale may be a predictive biomarker of OS in patients with T-cell lymphomas.

Could FAP-Targeted Molecular Imaging Replace 18F-FDG for Standard-of-Care Oncologic PET?

The predominant radiotracer in oncologic PET is 18F-FDG, to the point that many clinicians refer to 18F-FDG PET scans simply as “PET scans.” Numerous other radiotracers have been studied but only somatostatin receptor–targeted agents and prostate-specific membrane antigen (PSMA) have been

18F-FDG PET and a classifier algorithm reveal a characteristic glucose metabolic pattern in adult patients with moyamoya disease and vascular cognitive impairment.

Vascular cognitive impairment (VCI) is a critical issue in moyamoya disease (MMD). However, the glucose metabolic pattern in these patients is still unknown. This study aimed to identify the metabolic signature of cognitive impairment in patients with MMD using 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) and establish a classifier to identify VCI in patients with MMD. One hundred fifty-two patients with MMD who underwent brain 18F-FDG PET scans before surgery were enrolled and classified into nonvascular cognitive impairment (non-VCI, n = 52) and vascular cognitive impairment (VCI, n = 100) groups according to neuropsychological test results. Additionally, thirty-three health controls (HCs) were also enrolled. Compared to HCs, patients in the VCI group exhibited extensive hypometabolism in the bilateral frontal and cingulate regions and hypermetabolism in the bilateral cerebellum, while patients in the non-VCI group showed hypermetabolism only in the cerebellum and slight hypometabolism in the frontal and temporal regions. In addition, we found that the patients in the VCI group showed hypometabolism mainly in the left basal ganglia compared to those in the non-VCI group. The sparse representation-based classifier algorithm taking the SUVr of 116 Anatomical Automatic Labeling (AAL) areas as features distinguished patients in the VCI and non-VCI groups with an accuracy of 82.4%. This study demonstrated a characteristic metabolic pattern that can distinguish patients with MMD without VCI from those with VCI, namely, hypometabolic lesions in the left hemisphere played a more important role in cognitive decline in patients with MMD.

Characterization of orthotopic xenograft tumor of glioma stem cells (GSCs) on MRI, PET and immunohistochemical staining.

The orthotopic xenograft tumors of human glioma stem cells (GSCs) is a recent glioma model with genotype and phenotypic characteristics close to human gliomas. This study aimed to explore the imaging and immunohistochemical characteristics of GSCs gliomas. The rats underwent MRI and 18F-FDG PET scan in 6th-8th weeks after GSCs implantation. The MRI morphologic, DWI and PET features of the tumor lesions were assessed. In addition, the immunohistochemical features of the tumor tissues were further analyzed. Twenty-five tumor lesions were identified in 20 tumor-bearing rats. On structural MRI, the average tumor size was 30.04±17.31mm2, and the intensity was inhomogeneous in 76.00% (19/25) of the lesions. The proportion of the lesions mainly presented as solid, cystic and patchy tumor were 60.00% (15/25), 16.00% (4/25) and 24.00% (6/25), respectively. The boundary was unclear in 88.00% (22/25), and peritumoral mass effect was observed in 92.00% (23/25) of the lesions. On DWI, 80.00% (20/25) of the lesions showed increased intensity. Of the 14 lesions in the 11 rats underwent PET scan, 57.14% (8/14) showed increased FDG uptake. On immunohistochemical staining, the expression of Ki-67 was strong in all the lesions (51.67%±11.82%). Positive EGFR and VEGF expression were observed in 64.71% (11/17) and 52.94% (9/17) of the rats, whereas MGMT and HIF-1α showed negative expression in all the lesions. GSC gliomas showed significant heterogeneity and invasiveness on imaging, and exhibited strong expression of Ki-67, partial expression of EGFR and VEGF, and weak expression of MGMT and HIF-1α on immunohistochemical staining.

Regional differences in the reduction in cerebral FDG uptake induced by the ketogenic diet

BackgroundThe postulated benefits of the ketogenic diet in the management of multiple medical conditions have seen more patients who are in therapeutic ketosis attending 18F-FDG PET scans. This study aimed to investigate the effect of ketosis on cerebral glucose metabolism in a clinical PET scanning environment using 18F-FDG uptake as a surrogate marker.MethodsA retrospective audit was conducted of the brain 18F-FDG uptake in 52 patients who underwent PET scans for possible cardiac sarcoidosis or suspected intracardiac infection, following a ketogenic diet and prolonged fasting. SUVbw for whole brain and separate brain regions was compared with serum glucose and serum ketone body (beta-hydroxybutyrate) levels.ResultsThe expected negative association between serum glucose levels and whole brain 18F-FDG uptake was confirmed. A reduction in SUVbw due to increasing serum ketones levels was also observed that was independent of and in addition to the effects of glucose. The magnitude of the reduction in SUVbw related to serum glucose level and serum ketone level was found to be greater in the precuneus than in the cerebellum or whole brain.ConclusionIn a real-world clinical PET setting, cerebral 18F-FDG uptake appears to be affected by glycaemia and ketonaemia. This means when assessing the brain, both serum glucose and ketone levels need to be considered when SUVs are used to distinguish between pathologic and physiologic states. The magnitude of this effect appears to vary between different brain regions. This regional difference should be taken into consideration when selecting the appropriate brain region for SUV normalisation, particularly when undertaking database comparison in the assessment of dementia.

Detection of Cell Free Tumor DNA in Plasma of Patients with Large B-Cell Lymphoma of the Sanctuary Sites By Digital Droplet PCR

Background: Primary central nervous system lymphoma (PCNSL) and primary testicular lymphoma (PTL) are rare mature B-cell lymphoproliferative diseases that arise at immunological sanctuary sites. Cytomorphology (CM) and flow cytometry (FCM) analysis of spinal fluid is diagnostic in about 15% of PCNSL. In the remaining patients, a tumor biopsy is necessary, which carries the risk of complications. Response assessment in PCNSL and PTL relies on imaging studies. However, MRI scans and 18F-FDG PET-scans often cannot differentiate between active tumor and scar tissue. Hence, there is a need for non-invasive tools to aid in diagnosis and help in response assessment. PCNSL and PTL are characterized by a high incidence of hotspot mutations in MYD88 and CD79B. Detection of these mutations in cell free DNA (cfDNA) might aid in diagnosis and follow-up of disease activity in patients with PCNSL and PTL. Digital droplet PCR (ddPCR) offers a robust inexpensive tool with a fast turnaround time allowing sensitive detection of such hotspot mutations. Methods: Plasma was available for a total of 26 patients (20 PCNSL and 6 PTL) in the OncoLifes biobank of the University Medical Center Groningen. All patients gave informed consent. Corresponding tumor samples were available for 17 cases (14 PCNSL and 3 PTL). A ddPCR was performed for MYD88 L265P and in case of sufficient cfDNA also for CD79B Y196X (Bio-Rad Laboratories, Hercules, USA). The ddPCR Assay for CD79B Y196X targets the mutation Y196H c.586T>C, Y196D c.586T>G, Y196S c.587A>C, Y196C c.587A>G, and Y196F c.587A>T. The cut off value for the allele frequency (AF) used was ≥0.5%. Results: The MYD88 L265P and CD79B Y196X mutations were detected in 9/14 (64.2%) and 2/13 (15.4%) of the PCNSL tissue samples. The two CD79B Y196X positive tissue cases had a concomitant MYD88 L256P. The MYD88 L265P mutation was detected in 2/20 (10%) cfDNA samples. The CD79B Y196X mutation was detected in 1/19 (5.2%) cfDNA samples; remarkably, this mutation was not detected in the corresponding tissue sample. In PTL, MYD88 L265P mutations were detected in 3/3 of tumor tissues and a CD79B Y196X mutation was detected in 1/2 of tumor tissues, which co-occurred with the MYD88 L265P. In PTL, 2/6 (33.3%) and 1/6 (16.6%) cfDNA samples were positive for MYD88 L265P and CD79B Y196X. Overall, hotspot MYD88 and/or CD79B mutations were detected in cfDNA in 5 /26 cases (18.4%) (Figure 1). Figure 1. ddPCR results of MYD88 and CD79BConclusion: We explored the feasibility of using a combination of two common hotspot mutations in MYD88 and CD79B in PCNSL and PTL plasma samples to aid in diagnosis and disease monitoring. The data presented show that the value of ddPCR for hotspot mutations in plasma of PCNSL and PTL patients is limited. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Identifying the location of locoregional recurrences after definitive radiotherapy for head and neck cancer using metabolic parameters of baseline and mid-treatment 18F-FDG-PET scans.

Tumour recurrences after treatment of head and neck squamous cell carcinoma (HNSCC) are more likely to originate from regions of high-baseline FDG-PET uptake. Mid-treatment functional imaging can potentially predict for higher risk of tumour recurrence. The aim of this study is to correlate the location of locoregional tumour recurrence with baseline FDG-PET metabolic volumes and mid-treatment FDG-PET metabolic volumes in patients with HNSCC following definitive radiotherapy. A total of 23 patients with 26 local and/or regional recurrences underwent baseline (W0-PET) and mid-treatment (W3-PET) 18F-FDG PET scans as part of their radiotherapy. FDG-PET-based metabolic volumes (MTV20%, MTV40%, MTV60%, MTV80%, SUV2.5, SUVpeak and PET_EDGE) were delineated onto the FDG-PET scans. The recurrence nidus was identified on FDG-PET at the time of recurrence (REC-PET). DIR-based fusion was performed for REC-PET to W0-PET, and REC-PET to W3-PET. The location of the recurrence nidus was correlated with the FDG-PET volumes. Further analysis included a comparison of the recurrence density to FDG-PET metabolic volumes. Most recurrences occurred within the MTV20%, MTV40% and SUV 2.5 volumes. Sixty-nine per cent of recurrences (18 of 26) occurred within both the W0 MTV40% and W3 MTV40% volumes. A higher recurrence density was seen for iso-SUV contours closer to the maximum SUV for both W0 and W3. For a number of the FDG-PET volumes, including MTV20%, MTV40% and SUV2.5, the recurrence density was improved for W3 compared to W0, however, this improvement was small in magnitude. The average volume of MTV40% contours was considerably smaller than MTV20% and SUV2.5 contours. The metabolic parameters of SUV2.5, MTV20% and MTV40% delineated on the baseline and mid-treatment FDG-PET scans encompassed the majority of recurrences. The MTV40% is significantly smaller, hence, we prefer this volume for future dose escalation studies.

Development of a deep learning network for Alzheimer’s disease classification with evaluation of imaging modality and longitudinal data

Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer’s disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance. Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (N AD = 364, N control = 408) 3D 18F-FDG PET scans and 780 (N AD = 280, N control = 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer’s Disease Neuroimaging Initiative, plus an independent set of 104 (N AD = 63, N NC = 41) 18F-FDG PET scans (one per patient) for validation. Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on 18F-FDG PET, but not on T1-MRI. CNN model validation with an independent 18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability. Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using 18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.