18F-FDG PET Scans Research Articles

The role of 18fluorine-fluorodeoxyglucose positron emission tomography in the diagnosis, follow-up, and regulation of treatment in patients with cardiac sarcoidosis.

Abstract Background Cardiac sarcoidosis (CS) is a potentially fatal disease, commonly presenting with arrhythmias, heart failure, or sudden death. Diagnostic challenges persist, particularly due to limited sensitivity of endomyocardial biopsy (20-25%). Alternative diagnostic approaches, such as criteria-based diagnosis, have emerged. 18fluorine-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has gained prominence in diagnosing CS, monitoring treatment, and serving as a follow-up tool, although its precise role and efficacy remain unclear. Purpose This retrospective cohort study sought to characterize patients with CS who underwent at least one 118F-FDG-PET scan. Primary objectives were to explore the impact of 18F-FDG-PET on both the diagnosis and management of CS. Additionally, this study aimed to investigate any differences in patient outcomes based on the results of their first follow-up 18F-FDG-PET scan. Methods Medical records of 69 patients with CS who underwent at least one 18F-FDG-PET scan were analyzed. The study calculated the proportion of 18F-FDG PET scans influencing CS treatment and diagnosis, with 56 patients undergoing at least two scans. Among these, 53 patients were categorized into two groups based on their first follow-up scan results (group 1:no change/progress, group 2:regress). Statistical analyses included independent t-tests, the Mann-Whitney test, and Fisher's exact test. Outcome measures included death, heart transplantation or aborted cardiac death, with survival analyzed by Kaplan-Meier statistics. Results Among 69 patients, 204 18F-FDG-PET scans were conducted, with 47% directly influencing treatment decisions. Twenty-five patients were diagnosed with CS through 18F-FDG-PET. While no significant differences were observed regarding demographics and clinical features, the results suggested potential differences in event-free survival (p=0.084;Figure 1) and sex (p=0.08) between the groups. Conclusions 18F-FDG-PET has emerged as a pivotal tool in regulating treatment, diagnosing, and monitoring CS patients, playing a fundamental role in assessing treatment response. However, further studies are needed.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18 F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence-driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant. In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies.

The Role of 18F-FDG PET in the Diagnosis of Aortitis and Large Vessel Vasculitis

Background: 18F-FDG PET metabolic imaging provides significant help in the early diagnosis of inflammation of large and medium arteries, even before the appearance of structural vascular alterations. This retrospective study aims to evaluate the role of 18F-FDG PET in the diagnosis and management of large vessel vasculitis (LVV), including aortitis. Methodology: Fifty patients with clinical and/or biological suspicion of large vessel vasculitis but without a definitive diagnosis underwent 18F-FDG PET scanning. A qualitative visual scoring system and an average total vascular score (TVS) were used to compare 18F-FDG uptake in the vascular walls with that in the liver. Patients who had been on corticosteroids for more than 8 days were excluded from the study. Results: The study included 50 patients, 27 women and 23 men, with an average age of 65.8 ± 14.5 years. The analysis of 18F-FDG PET images allowed for the classification of subjects into two distinct groups. Among the 50 subjects, 16 (32%) showed higher 18F-FDG uptake in the vascular walls compared to hepatic uptake (PET-positive group), while 34 (68%) showed lower uptake (PET-negative group). In the positive group, 75% were diagnosed with Horton’s disease, and the remaining 25% with other inflammatory diseases such as Takayasu arteritis or polyarteritis nodosa. None of the subjects in the positive group had an aortic aneurysm. All patients with a positive 18F-FDG PET had elevated levels of C-reactive protein. The TVS obtained for aortitis alone was 5.9 ± 4.7, while the score for more global vasculitis was obviously higher, at 10.4 ± 6.3. Conclusion: 18F-FDG PET has become a valuable tool in the diagnosis of large vessel vasculitis, particularly in the early stages of the disease. It also allows for mapping of arterial involvement and estimation of disease severity using the TVS.

Reversal of Basal Ganglia Hypermetabolism Following Surgical Removal of Adrenocorticotropic Hormone-Producing Lung Carcinoid in a Patient of Cushing Syndrome With Unusual Presentation of Acute Psychosis: Proof of Concept.

A 34-year-old woman who presented with severe psychosis and clinical features of Cushing syndrome underwent 18F-FDG PET/CT that revealed hypermetabolic lung lesion along with predominantly increased metabolism of bilateral basal ganglia, a scintigraphic correlate of acute psychosis. The lesion was surgically excised and histopathologically proven to be adrenocorticotropic hormone-producing lung carcinoid. Posttreatment 18F-FDG PET scan showed restoration of normal brain metabolism with complete reversal of psychosis. Even though rare, one should be aware of psychosis as an initial presentation of Cushing syndrome and use of 18F-FDG PET/CT for mapping brain metabolism as shown in this case.

Effects of Early and Late Cranioplasty on Neurocognitive Outcome and Cerebral Glucose Metabolism using PET Scan - A Comparative Study.

Brain protection and cosmetic aspects are the major indications of cranioplasty (CP) after decompressive craniectomy. CP can avoid the recurrence of brain damage, achieve the plastic effect, protect the patient from seizures, and relieve the syndrome of trephine. This was a prospective, observational study done over a period of 2 years from April 2017 to April 2019 in the Department of Neurosurgery at Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati. Patients of age group 20-60 years who underwent CP after decompressive craniectomy for traumatic brain injury or cerebrovascular accidents with refractory intracranial hypertension were included. The study population was divided into two groups: early and late CP groups. Neurocognitive assessment was done 72 h before and 3 months after CP by mini-mental state examination (MMSE), Glasgow outcome score (GOS), and PGI battery of brain dysfunction (PGIBBD) scores. Cerebral glucose metabolism was assessed by 18F-FDG PET scan. In both early and late CP groups, there was a highly significant difference between the mean pre- and postoperative values of MMSE, GOS, and PGIBBD, suggesting significant improvement in neurocognitive parameters of patients postoperatively. There was no significant difference between early and late CP groups for mean standard uptake values (SUVs) on PET scan for both affected (P-value- 0.40) and nonaffected (P-value- 0.30) sides. CP improves the cerebral metabolism and neurocognitive outcome, weather it is done early or late.

Quantitative Total-Body Imaging of Blood Flow with High Temporal Resolution Early Dynamic 18F-Fluorodeoxyglucose PET Kinetic Modeling.

The first two minutes of dynamic PET scans were reconstructed at high temporal resolution (60×1 s, 30×2 s) to resolve the rapid passage of the radiotracer through blood vessels. In contrast to existing methods that use blood-to-tissue transport rate ( ) as a surrogate of blood flow, our method directly estimates blood flow using a distributed kinetic model (adiabatic approximation to the tissue homogeneity model; AATH). To validate our 18F-FDG measurements of blood flow against a flow radiotracer, we analyzed total-body dynamic PET images of six human participants scanned with both 18F-FDG and 11C-butanol. An additional thirty-four total-body dynamic 18F-FDG PET scans of healthy participants were analyzed for comparison against literature blood flow ranges. Regional blood flow was estimated across the body and total-body parametric imaging of blood flow was conducted for visual assessment. AATH and standard compartment model fitting was compared by the Akaike Information Criterion at different temporal resolutions. 18F-FDG blood flow was in quantitative agreement with flow measured from 11C-butanol across same-subject regional measurements (Pearson R=0.955, p<0.001; linear regression y=0.973x-0.012), which was visually corroborated by total-body blood flow parametric imaging. Our method resolved a wide range of blood flow values across the body in broad agreement with literature ranges (e.g., healthy cohort average: 0.51±0.12 ml/min/cm3 in the cerebral cortex and 2.03±0.64 ml/min/cm3 in the lungs, respectively). High temporal resolution (1 to 2 s) was critical to enabling AATH modeling over standard compartment modeling. Total-body blood flow imaging was feasible using early-dynamic 18F-FDG PET with high-temporal resolution kinetic modeling. Combined with standard 18F-FDG PET methods, this method may enable efficient single-tracer flow-metabolism imaging, with numerous research and clinical applications in oncology, cardiovascular disease, pain medicine, and neuroscience.

Spatial-temporal dynamic evolution of lewy body dementia by metabolic PET imaging.

Lewy body dementia (LBD) is a neurodegenerative disease with high heterogeneity and complex pathogenesis. Our study aimed to use disease progression modeling to uncover spatial-temporal dynamic evolution of LBD in vivo, and to explore differential profiles of clinical features, glucose metabolism, and dopaminergic function among different evolution-related subtypes. A total of 123 participants (31 healthy controls and 92 LBD patients) who underwent 18F-FDG PET scans were retrospectively enrolled. 18F-FDG PET-based Subtype and Stage Inference (SuStaIn) model was established to illustrate spatial-temporal evolutionary patterns and categorize relevant subtypes. Then subtypes and stages were further related to clinical features, glucose metabolism, and dopaminergic function of LBD patients. This 18F-FDG PET imaging-based approach illustrated two distinct patterns of neurodegenerative evolution originating from the neocortex and basal ganglia in LBD and defined them as subtype 1 and subtype 2, respectively. There were obvious differences between subtypes. Compared with subtype 1, subtype 2 exhibited a greater proportion of male patients (P = 0.045) and positive symptoms such as visual hallucinations (P = 0.033) and fluctuating cognitions (P = 0.033). Cognitive impairment, metabolic abnormalities, dopaminergic dysfunction and progression were all more severe in subtype 2 (all P < 0.05). In addition, a strong association was observed between SuStaIn subtypes and two clinical phenotypes (Parkinson's disease dementia and dementia with Lewy bodies) (P = 0.005). Our findings based on 18F-FDG PET and data-driven model illustrated spatial-temporal dynamic evolution of LBD and categorized novel subtypes with different evolutionary patterns, clinical and imaging features in vivo. The evolution-related subtypes are associated with LBD clinical phenotypes, which supports the perspective of existence of distinct entities in LBD spectrum.

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations

Objective. To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed to 18F-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children. Approach. Data of 29 Chinese pediatric patients undergoing whole-body 18F-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities. Absorbed radiation dose for both CT and PET component were calculated using Monte Carlo simulations. Regression models were fitted to time integrated activity coefficient (TIAC) and organ absorbed dose for each patient. Main results. TIACs of all the organs in our compartment model and the organ dose for 12 organs were correlated with patients’ weight. Young children have significantly large uptake in brain compared to adults. The distinctions of anatomical and biological characteristics between Chinese and Caucasian children contribute to variations in the absorbed dose of 18F-FDG PET scans. PET contributed more in organ dose than CT did in most organs, especially in brain and bladder. The average effective dose (± SD) was 4.5 mSv (± 1.12 mSv), 7.8 mSv (± 3.2 mSv) and 12.3 mSv (± 3.5 mSv) from CT, PET and their sum respectively. PET contributed 1.7 times higher than CT. Significance. To the best of our knowledge, this work represents the first attempt to estimate patient-specific radiation doses from PET/CT for Chinese pediatric patients. TIACs derived from our methodology in both age groups exhibited significant differences from the that reported in ICRP 128. Substantial differences in absorbed and effective doses were observed between Chinese and Caucasian children across all age groups. These disparities are attributed to markedly distinct anatomical and pharmacokinetic characteristics among adults and pediatric patients, and different racial groups. The application of data derived from adults to pediatric patients introduces considerable uncertainty. Our methodology offers a valuable approach not only for estimating pharmacokinetic characteristics and patient-specific radiation doses in pediatric patients undergoing 18F-FDG studies but also for other cohorts with similar characteristics.

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls.

This study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls. A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set. The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients. The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

Evaluating Prognosis of Gastrointestinal Metastatic Neuroendocrine Tumors: Constructing a Novel Prognostic Nomogram Based on NETPET Score and Metabolic Parameters from PET/CT Imaging.

The goal of this study is to compare the prognostic performance of NETPET scores, based on gallium-68 DOTANOC (68Ga-DOTANOC) and fluorine-18 fluorodeoxyglucose (18F-FDG) Positron Emission Tomography-Computed Tomography (PET-CT), and PET-CT metabolic parameters in metastatic gastrointestinal neuroendocrine tumors (GI-NET), while constructing and validating a nomogram derived from dual-scan PET-CT. In this retrospective study, G1-G3 GI-NET patients who underwent 68Ga-DOTANOC and 18F-FDG PET scans were enrolled and divided into training and internal validation cohorts. Three grading systems were constructed based on NETPET scores and standardized uptake value maximum (SUVmax). LASSO regression selected variables for a multivariable Cox model, and nomograms predicting progression-free survival (PFS) and overall survival (OS) were created. The prognostic performance of these systems was assessed using time-dependent receiver-operating characteristic (ROC) curves, concordance index (C-index), and other methods. Nomogram evaluation involved calibration curves, decision curve analysis (DCA), and the aforementioned methods in both cohorts. In this study, 223 patients (130 males; mean age ± SD: 52.6 ± 12 years) were divided into training (148) and internal validation (75) cohorts. Dual scans were classified based on NETPET scores (D1-D3). Single 68Ga-DOTANOC and 18F-FDG PET-CT scans were stratified into S1-S3 and F1-F3 based on SUVmax. The NETPET score-based grading system demonstrated the best OS and PFS prediction (C-index, 0.763 vs. 0.727 vs. 0.566). Nomograms for OS and PFS exhibited superior prognostic performance in both cohorts (all AUCs > 0.8). New classification based on NETPET score predicts patient OS/PFS best. PET-CT-based nomograms show accurate OS/PFS forecasts.

Decomposed FDG PET-based phenotypic heterogeneity predicting clinical prognosis and decision-making in temporal lobe epilepsy patients.

This study utilized a data-driven Bayesian model to automatically identify distinct latent disease factors represented by overlapping glucose metabolism patterns from 18F-Fluorodeoxyglucose PET (18F-FDG PET) to analyze heterogeneity among patients with TLE. We employed unsupervised machine learning to estimate latent disease factors from 18F-FDG PET scans, representing whole-brain glucose metabolism patterns in seventy patients with TLE. We estimated the extent to which multiple distinct factors were expressed within each participant and analyzed their relevance to epilepsy burden, including seizure onset, duration, and frequency. Additionally, we established a predictive model for clinical prognosis and decision-making. We identified three latent disease factors: hypometabolism in the unilateral temporal lobe and hippocampus (factor 1), hypometabolism in bilateral prefrontal lobes (factor 2), and hypometabolism in bilateral temporal lobes (factor 3), variably co-expressed within each patient. Factor 3 demonstrated the strongest negative correlation with the age of onset and duration (r = - 0.33, - 0.38 respectively, P < 0.05). The supervised classifier, trained on latent disease factors for predicting patient-specific antiepileptic drug (AED) responses, achieved an area under the curve (AUC) of 0.655. For post-surgical seizure outcomes, the AUC was 0.857, and for clinical decision-making, it was 0.965. Decomposing 18F-FDG PET-based phenotypic heterogeneity facilitates individual-level predictions relevant to disease monitoring and personalized therapeutic strategies.

Glucose Metabolism of Hippocampal Subfields in Medial Temporal Lobe Epilepsy.

Reduced glucose metabolism in the hippocampus is commonly observed in cases of medial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS). Glucose metabolism among the various hippocampal subfields has not been thoroughly investigated. This study examined 29 patients (18 females; 15-58 years) diagnosed with HS who underwent surgery for drug-resistant epilepsy. FreeSurfer 7.1.1 was used in the processing of MRI data and 18 F-FDG PET scans to derive volumetric data and the FDG SUVr in the whole hippocampus and hippocampal subfields, including the CA1, CA2-4, granule cell and molecular layer of the dentate gyrus (GC-ML-DG), and subiculum. Asymmetries in the volume and SUVr between the 2 sides from the subfields of the hippocampus were defined in terms of an asymmetry index. Comparisons of the asymmetry index among these regions were performed. The correlations between asymmetry index values and postoperative outcomes and presurgical neuropsychological test results were also evaluated. The CA1, CA2-4, subiculum, GC-ML-DG, and whole hippocampus presented reductions in volume and hypometabolism ipsilateral to MTLE. Asymmetries in volume and SUVr were significantly less pronounced in the CA1 and subiculum than in the CA2-4 or GC-ML-DG. Postoperative seizure outcomes were not correlated with the asymmetry index for volume or SUVr in any hippocampal subfield. In cases of left MTLE, scores of immediate logical memory and delayed logical memory were positively correlated with the asymmetry index for SUVr in the following subfields: CA1 ( R = 0.829, P = 0.021; R = 0.770, P = 0.043), CA2-4 ( R = 0.825, P = 0.022; R = 0.894, P = 0.007), subiculum ( R = 0.882, P = 0.009; R = 0.853, P = 0.015), GC-ML-DG ( R = 0.850, P = 0.015; R = 0.796, P = 0.032), and whole hippocampus ( R = 0.841, P = 0.018; R = 0.822, P = 0.023). In cases of right MTLE, the scores for delayed face memory were positively correlated with the asymmetry index for SUVr in the subiculum ( R = 0.935, P = 0.006). In cases of HS, changes in glucose metabolism levels varied among the hippocampal subfields. Asymmetries in glucose metabolism among the CA-1, CA2-4, subiculum, and GC-ML-DG subregions were correlated with scores for verbal memory among patients with left MTLE. Asymmetric glucose metabolism in the subiculum was also correlated with visual memory scores among patients with right MTLE.

Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders.

The Next Move in Movement Disorders (NEMO) study is an initiative aimed at advancing our understanding and the classification of hyperkinetic movement disorders, including tremor, myoclonus, dystonia, and myoclonus-dystonia. The study has two main objectives: (a) to develop a computer-aided tool for precise and consistent classification of these movement disorder phenotypes, and (b) to deepen our understanding of brain pathophysiology through advanced neuroimaging techniques. This protocol review details the neuroimaging data acquisition and preprocessing procedures employed by the NEMO team to achieve these goals. To meet the study's objectives, NEMO utilizes multiple imaging techniques, including T1-weighted structural MRI, resting-state fMRI, motor task fMRI, and 18F-FDG PET scans. We will outline our efforts over the past 4 years to enhance the quality of our collected data, and address challenges such as head movements during image acquisition, choosing acquisition parameters and constructing data preprocessing pipelines. This study is the first to employ these neuroimaging modalities in a standardized approach contributing to more uniformity in the analyses of future studies comparing these patient groups. The data collected will contribute to the development of a machine learning-based classification tool and improve our understanding of disorder-specific neurobiological factors. Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.

An International Survey Investigating the Incidence and Management of Brown Fat Uptake on 18F-FDG PET/CT at Children's Hospitals and Interventions for Mitigation.

Brown fat can present challenges in patients with cancer who undergo 18F-FDG PET scans. Uptake of 18F-FDG by brown fat can obscure or appear similar to active oncologic lesions, causing clinical challenges in PET interpretation. Small, retrospective studies have reported environmental and pharmacologic interventions for suppressing brown fat uptake on PET; however, there is no clear consensus on best practices. We sought to characterize practice patterns for strategies to mitigate brown fat uptake of 18F-FDG during PET scanning. Methods: A survey was developed and distributed via e-mail LISTSERV to members of the Children's Oncology Group diagnostic imaging committee, the Society for Nuclear Medicine and Molecular Imaging pediatric imaging council, and the Society of Chiefs of Radiology at Children's Hospitals between April 2022 and February 2023. Responses were stored anonymously in REDCap, aggregated, and summarized using descriptive statistics. Results: Fifty-five complete responses were submitted: 51 (93%) faculty and fellow-level physicians, 2 (4%) technologists, and 2 (4%) respondents not reporting their rank. There were 43 unique institutions represented, including 5 (12%) outside the United States. Thirty-eight of 41 (93%) institutions that responded on environmental interventions reported using warm blankets in the infusion and scanning rooms. Less than a third (n = 13, 30%) of institutions reported use of a pharmacologic intervention, with propranolol (n = 5, 38%) being most common, followed by fentanyl (n = 4, 31%), diazepam (n = 2, 15%), and diazepam plus propranolol (n = 2, 15%). Selection criteria for pharmacologic intervention varied, with the most common criterion being brown fat uptake on a prior scan (n = 6, 45%). Conclusion: Clinical practices to mitigate brown fat uptake on pediatric 18F-FDG PET vary widely. Simple environmental interventions including warm blankets or increasing the temperature of the injection and scanning rooms were not universally reported. Less than a third of institutions use pharmacologic agents for brown fat mitigation.

Prevention of activated brown adipose tissue on 18F-FDG-PET scans of young lymphoma patients: results of an ancillary study within the EuroNet-PHL-C2 trial

Activated brown fat (aBAT) is known to affect the evaluation of 18F-FDG PET scans, especially in young patients. The aim of this study was to determine factors influencing the occurrence of aBAT, and to investigate the effectiveness of the two preventive measures, warming and beta-blocker (propranolol) administration. Five-hundred-twenty-eight 18F-FDG-PET scans of 241 EuroNet-PHL-C2 trial patients from 41 nuclear medicine departments in Germany and Czech Republic were screened for aBAT. The occurrence of aBAT was analyzed with patient characteristics (age, sex, body mass index, predisposition to aBAT), weather data at the day of 18F-FDG PET scanning as well as the preventive measures taken. Potentially important factors from univariate analyses were included into a logistic regression model. Warming as a preventive measure was used in 243 18F-FDG-PET scans, propranolol was administered in 36, warming and propranolol were combined in 84, and no preventive measures were taken in 165 scans. Whereas age, sex and body mass index had no clear impact, there was an individual predisposition to aBAT. Logistic regression model revealed that the frequency of aBAT mainly depends on the outside temperature (p = 0.005) and can be effectively reduced by warming (p = 0.004), the administration of unselective beta-blocker or the combination of both. Warming is a simple, cheap and non-invasive method to reduce the frequency of aBAT. However, the effect of warming decreases with increasing outside temperatures. Administration of propranolol seems to be equally effective and provides advantages whenever the positive effect of warming is compromised. The combination of both preventive measures could have an additive effect.

MRI or 18F-FDG PET for Brain Age Gap Estimation: Links to Cognition, Pathology, and Alzheimer Disease Progression.

Deviations of brain age from chronologic age, known as the brain age gap (BAG), have been linked to neurodegenerative diseases such as Alzheimer disease (AD). Here, we compare the associations of MRI-derived (atrophy) or 18F-FDG PET-derived (brain metabolism) BAG with cognitive performance, neuropathologic burden, and disease progression in cognitively normal individuals (CNs) and individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). Methods: Machine learning pipelines were trained to estimate brain age from 185 matched T1-weighted MRI or 18F-FDG PET scans of CN from the Alzheimer's Disease Neuroimaging Initiative and validated in external test sets from the Open Access of Imaging and German Center for Neurodegenerative Diseases-Longitudinal Cognitive Impairment and Dementia studies. BAG was correlated with measures of cognitive performance and AD neuropathology in CNs, SCD subjects, and MCI subjects. Finally, BAG was compared between cognitively stable and declining individuals and subsequently used to predict disease progression. Results: MRI (mean absolute error, 2.49 y) and 18F-FDG PET (mean absolute error, 2.60 y) both estimated chronologic age well. At the SCD stage, MRI-based BAG correlated significantly with beta-amyloid1-42 (Aβ1-42) in cerebrospinal fluid, whereas 18F-FDG PET BAG correlated with memory performance. At the MCI stage, both BAGs were associated with memory and executive function performance and cerebrospinal fluid Aβ1-42, but only MRI-derived BAG correlated with phosphorylated-tau181/Aβ1-42 Lastly, MRI-estimated BAG predicted MCI-to-AD progression better than 18F-FDG PET-estimated BAG (areas under the curve, 0.73 and 0.60, respectively). Conclusion: Age was reliably estimated from MRI or 18F-FDG PET. MRI BAG reflected cognitive and pathologic markers of AD in SCD and MCI, whereas 18F-FDG PET BAG was sensitive mainly to early cognitive impairment, possibly constituting an independent biomarker of brain age-related changes.

Reduction of Metabolic Active Tumor Volume Prior to CAR T-Cell Therapy Improves Survival Outcomes in Patients with Large B-Cell Lymphoma

Introduction CD19-targeted chimeric antigen receptor (CAR) T-cell therapy has become the standard 2 nd or 3 rd line therapy for patients with relapsed or refractory Large B-cell lymphoma (r/r LBCL). As part of this treatment bridging therapy is frequently used between T-cell apheresis and CAR T-cell infusion in order to limit disease progression, diminish lymphoma-related symptoms and/or reduce tumor volume. Bridging therapy has shown to be feasible, safe and effective in patients prior to infusion. In addition, a lower tumor volume is associated with better treatment outcomes and less toxicity. However, the exact reduction and dynamics in metabolic active tumor volume (MATV) are unknown. Here we investigated the relation of changes in pre-apheresis (baseline) MATV, based on 18F-FDG PET scans, and pre-lymphodepleting chemotherapy (pre-LD) MATV with CAR T-cell outcomes and toxicities in patients with r/r LBCL. Methods Baseline and pre-LD MATVs and PET characteristics were calculated semi-automatically of patients who were treated with CAR T-cell therapy. A Standardized Uptake Value (SUV) threshold of 2.5 was used to maintain robustness and avoid MATV underestimation. Bridging strategies consisted of radiotherapy, chemotherapy, or a combination of both. Primary endpoints were duration of response (DOR) and overall survival (OS). Secondary endpoints included cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). The Youden index was used to identify the optimal cut-off for dividing patients into low- and high-MATV groups to predict DOR at 2 years. Two-year DOR and OS rates were estimated using Kaplan-Meier curves and the log-rank test was utilized to compare the differences among low- and high-MATV patient groups. Univariable analysis was performed with Cox regression for DOR and OS, and with logistic regression for CRS and ICANS. Results Seventy-four patients with LBCL and treated with CAR T-cell therapy were included in the study. For all patients baseline 18F-FDG PET scans were available, whereof in 68 patients the pre-LD scans. The median baseline MATV was 180 cc (IQR 64 - 426) with a median SUV max of 18.8 (IQR 11.9 - 28.1) and increased to 241 cc (IQR 75 - 525) at pre-LD, with a median SUV max of 21.0 (IQR 12.8 - 31.3). The optimal cut-offs for low- and high-MATV patient groups at baseline and pre-LD were 190 cc and 480 cc, respectively. Patients with a low MATV baseline had a better DOR (Figure 1A) and OS compared to high MATV baseline patients (both p &amp;lt; 0.001). Patients with a high MATV baseline that was reduced to a low MATV pre-LD showed a significant improvement in DOR (Figure 1B) and OS, compared to patients with a high MATV pre-LD ( p = 0.036 for DOR; p = 0.015 for OS). Univariable analysis showed that a high baseline MATV was associated with a worse DOR (HR 6.43 CI [2.15 - 19.20]; p &amp;lt; 0.001) and OS (HR 6.97 CI [2.33 - 20.80]; p &amp;lt; 0.001). A high pre-LD MATV was also associated with a worse DOR (HR 4.86 CI [1.98 - 12.00]; p &amp;lt; 0.001) and OS (HR 5.87 [2.41 - 14.30]; p &amp;lt; 0.001). Other significant associations with DOR and OS were observed for pre-LD lactate dehydrogenase (LDH), the IPI score for DOR exclusively, and gender and bridging therapy for OS exclusively. Conclusions We showed that patients with a low MATV compared to a high MATV at baseline have better survival outcomes and less toxicity. Additionally, reducing tumor volume by applying bridging therapy in patients with a high baseline MATV, results in a significant better DOR and OS. Reducing the MATV before CAR T-cell infusion is therefore essential in CAR T-cell therapy patient care and provides a rationale for the use of more intensive bridging therapies.

Mitigating SUV uncertainties using total body PET imaging

PurposeStandardised uptake values (SUV) are commonly used to quantify 18F-FDG lesion uptake. However, SUVs may suffer from several uncertainties and errors. Long-axial field-of-view (LAFOV) PET/CT systems might enable image-based quality control (QC) by deriving 18F-FDG activity and weight from total body (TB) 18F-FDG PET images. In this study, we aimed to develop these image-based QC to reduce errors and mitigate SUV uncertainties.MethodsTwenty-five out of 81 patient scans from a LAFOV PET/CT system were used to determine regression fits for deriving of image-derived activity and weight. Thereafter, the regression fits were applied to 56 independent 18F-FDG PET scans from the same scanner to determine if injected activity and weight could be obtained accurately from TB and half-body (HB) scans. Additionally, we studied the impact of image-based values on the precision of liver SUVmean and lesion SUVpeak. Finally, 20 scans were acquired from a short-axial field-of-view (SAFOV) PET/CT system to determine if the regression fits also applied to HB scans from a SAFOV system.ResultsBoth TB and HB 18F-FDG activity and weight significantly predicted reported injected activity (r = 0.999; r = 0.984) and weight (r = 0.999; r = 0.987), respectively. After applying the regression fits, 18F-FDG activity and weight were accurately derived within 4.8% and 3.2% from TB scans and within 4.9% and 3.1% from HB, respectively. Image-derived values also mitigated liver and lesion SUV variability compared with reported values. Moreover, 18F-FDG activity and weight obtained from a SAFOV scanner were derived within 6.7% and 4.5%, respectively.Conclusion18F-FDG activity and weight can be derived accurately from TB and HB scans, and image-derived values improved SUV precision and corrected for lesion SUV errors. Therefore, image-derived values should be included as QC to generate a more reliable and reproducible quantitative uptake measurement.

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.