Fluorodeoxyglucose PET Imaging Research Articles

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness

Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach.

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering

Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First, we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k -means and Gaussian mixture model (GMM), for further analyses. We combined k -means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). Then, we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F] fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k -means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k -means combined with PCA the most promising candidate for further development. However, even with the best methods, the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images.

Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.

Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, and segmentation. Machine learning algorithms such as CNNs or Transformers are subject to an inductive bias, which can have a significant impact on the performance of machine learning models. This is especially relevant for medical image segmentation applications where limited training data are available, and a model's inductive bias should help it to generalize well. In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For performance assessment, 272 FDG PET-CT scans of a clinical trial (ACRIN 6685) were utilized, which includes a total of 650 lesions (primary: 272 and secondary: 378). The image data used are highly diverse and representative for clinical use. For performance analysis, several error metrics were utilized. The achieved Dice coefficient ranged from 0.833 to 0.809 with the best performance being achieved by CNN-based approaches. U-Net-CBAM, which utilizes spatial and channel attention, showed several advantages for smaller lesions compared to the standard U-Net. Furthermore, our results provide some insight regarding the image features relevant for this specific segmentation application. In addition, results highlight the need to utilize primary as well as secondary lesions to derive clinically relevant segmentation performance estimates avoiding biases.

Optimization of Metabolic Tumor Volume as a Prognostic Marker in CAR T-Cell Therapy for Aggressive Large B-cell NHL

BackgroundCD19-targeted chimeric antigen receptor (CAR) T-cell therapy has become a standard of care in relapsed/refractory (R/R) aggressive large B-cell non-Hodgkin lymphomas (B-NHL) though the majority of recipients do not receive durable disease benefit, prompting the need to better define risk factors for relapse/progression. ObjectivesWe performed a single-center, retrospective analysis of patients treated with commercial CAR T-cell therapy to evaluate the impact of tumor burden, as measured by whole-body metabolic tumor volume (MTV) from 18F fluorodeoxyglucose PET imaging, on treatment outcomes. Study DesignSixty-one patients treated with CAR T-cell therapy for R/R B-NHL between May 2016 and November 2021 were included. ResultsUsing a receiver operating characteristic curve-based MTV optimization cutoff of 450 mL, 1-year progression-free survival (PFS) was 22% for high MTV versus 54% for low MTV (P < .01), and 1-year overall survival (OS) was 37% and 73%, respectively (P = .01). In a subset of 46 patients, residual MTV of less than 106 mL at the day 30 (D30) disease assessment was associated with significantly improved outcomes (1-year OS 85% vs. 13%, P < .01). Incorporation of pretreatment MTV to the International Prognostic Index (IPI) scoring system significantly distinguished 2-year PFS and OS outcomes by 3 risk groups. ConclusionsOur findings suggest that both pretreatment and D30 MTV are predictive of outcomes among R/R B-NHL patients treated with CAR T-cell therapy. These data indicate that efforts to reduce pretreatment tumor burden may improve longitudinal clinical outcomes. Furthermore, D30 postinfusion MTV quantification may aid clinicians in optimally identifying patients at high-risk for progression, and in whom closer disease monitoring should be considered. MTV also adds prognostic value to patients with high-risk IPI and holds promise for incorporation in novel risk scoring systems which can identify patients prior to CAR T-cell therapy at highest risk of adverse outcomes.

Multimodal Imaging for Validation and Optimization of Ion Channel-Based Chemogenetics in Nonhuman Primates.

Chemogenetic tools provide an opportunity to manipulate neuronal activity and behavior selectively and repeatedly in nonhuman primates (NHPs) with minimal invasiveness. Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are one example that is based on mutated muscarinic acetylcholine receptors. Another channel-based chemogenetic system available for neuronal modulation in NHPs uses pharmacologically selective actuator modules (PSAMs), which are selectively activated by pharmacologically selective effector molecules (PSEMs). To facilitate the use of the PSAM/PSEM system, the selection and dosage of PSEMs should be validated and optimized for NHPs. To this end, we used a multimodal imaging approach. We virally expressed excitatory PSAM (PSAM4-5HT3) in the striatum and the primary motor cortex (M1) of two male macaque monkeys, and visualized its location through positron emission tomography (PET) with the reporter ligand [18F]ASEM. Chemogenetic excitability of neurons triggered by two PSEMs (uPSEM817 and uPSEM792) was evaluated using [18F]fluorodeoxyglucose-PET imaging, with uPSEM817 being more efficient than uPSEM792. Pharmacological magnetic resonance imaging (phMRI) showed that increased brain activity in the PSAM4-expressing region began ∼13 min after uPSEM817 administration and continued for at least 60 min. Our multimodal imaging data provide valuable information regarding the manipulation of neuronal activity using the PSAM/PSEM system in NHPs, facilitating future applications.SIGNIFICANCE STATEMENT Like other chemogenetic tools, the ion channel-based system called pharmacologically selective actuator module/pharmacologically selective effector molecule (PSAM/PSEM) allows remote manipulation of neuronal activity and behavior in living animals. Nevertheless, its application in nonhuman primates (NHPs) is still limited. Here, we used multitracer positron emission tomography (PET) imaging and pharmacological magnetic resonance imaging (phMRI) to visualize an excitatory chemogenetic ion channel (PSAM4-5HT3) and validate its chemometric function in macaque monkeys. Our results provide the optimal agonist, dose, and timing for chemogenetic neuronal manipulation, facilitating the use of the PSAM/PSEM system and expanding the flexibility and reliability of circuit manipulation in NHPs in a variety of situations.

866-P: Quantitative Analysis of Metformin-Induced Glucose Excretion in the Intestine

The enhancement of the uptake of 18F-labeled fluorodeoxyglucose (FDG) during [18F]FDG PET is a recently found metformin action in the gut. Taking advantage of PET-MRI, which provides better tissue registration and soft-tissue contrast than conventional PET-CT, we revealed that metformin enhanced FDG accumulation in the intestinal lumen. We now quantitatively analyzed the phenomenon with a newly developed protocol of PET-MRI. We also analyzed the intestinal glucose dynamics using 13C-labeled glucose and mass spectrometry in mice. We adopted imaging sequences of MR enterography to create 3D images of the intestinal tract, then superimposed the [18F]FDG PET images to obtain the 3D distribution of radioactivity. We conducted this test on type 2 diabetes subjects treated (N=5) or not (control, N=5) with metformin. Radioactivity was detected in the wall and lumen at similar extents in the control. However, in the metformin group, the radioactivity in the lumen, but not the wall, was three to four-fold greater than that in the control. We mathematically analyzed the data using a compartment model and calculated the glucose excretion rate (GER). GER of the control and metformin groups were 6.8 ± 1.2 mg/min and 26.1 ± 2.9 mg/min, respectively, the latter being comparable to glucose excretion into the urine induced by SGLT2 inhibitors. For mouse experiments, we intravenously injected 13C-glucose into C57/B6 mice and analyzed the feces 1 hour after injection with a gas chromatography-quadrupole mass spectrometry. We could not detect 13C-glucose in the feces, possibly because gut microbiota metabolizes glucose in the intestine. However, the content of 13C in short-chain fatty acids (SCFAs) was increased by the injection of 13C-glucose, and metformin augmented the incorporation of 13C into SCFAs. These results suggest that glucose in the circulation transfers to the intestinal lumen at a considerable rate and is metabolized by microbiota, at least partly, into SCFAs and that metformin augments the processes. Disclosure Y.Hosokawa: None. K.Sakaguchi: Research Support; Sumitomo Dainippon Pharma Co., Ltd. J.Ito: None. K.Sugawara: None. Y.Morita: None. W.Ogawa: Advisory Panel; Abbott Japan Co., Ltd., Research Support; Boehringer Ingelheim Japan, Inc., Eli Lilly Japan K.K., Novo Nordisk, Teijin Pharma Limited, Kowa Company, Ltd., Sumitomo Dainippon Pharma Co., Ltd., Abbott Japan Co., Ltd., Speaker's Bureau; Sumitomo Dainippon Pharma Co., Ltd. Funding Japan Society for the Promotion of Science (21K08578, 22K18393)

Severe cholinergic terminal loss in newly diagnosed dementia with Lewy bodies.

Cholinergic changes play a fundamental role in the natural history of dementia with Lewy bodies and Lewy body disease in general. Despite important achievements in the field of cholinergic research, significant challenges remain. We conducted a study with four main objectives: (i) to examine the integrity of cholinergic terminals in newly diagnosed dementia with Lewy bodies; (ii) to disentangle the cholinergic contribution to dementia by comparing cholinergic changes in Lewy body patients with and without dementia; (iii) to investigate the in vivo relationship between cholinergic terminal loss and atrophy of cholinergic cell clusters in the basal forebrain at different stages of Lewy body disease; and (iv) to test whether any asymmetrical degeneration in cholinergic terminals would correlate with motor dysfunction and hypometabolism. To achieve these objectives, we conducted a comparative cross-sectional study of 25 newly diagnosed dementia with Lewy bodies patients (age 74 ± 5 years, 84% male), 15 healthy control subjects (age 75 ± 6 years, 67% male) and 15 Parkinson's disease patients without dementia (age 70 ± 7 years, 60% male). All participants underwent 18F-fluoroetoxybenzovesamicol PET and high-resolution structural MRI. In addition, we collected clinical 18F-fluorodeoxyglucose PET images. Brain images were normalized to standard space and regional tracer uptake and volumetric indices of basal forebrain degeneration were extracted. Patients with dementia showed spatially distinct reductions in cholinergic terminals across the cerebral cortex, limbic system, thalamus and brainstem. Also, cholinergic terminal binding in cortical and limbic regions correlated quantitatively and spatially with atrophy of the basal forebrain. In contrast, patients without dementia showed decreased cholinergic terminal binding in the cerebral cortex despite preserved basal forebrain volumes. In patients with dementia, cholinergic terminal reductions were most severe in limbic regions and least severe in occipital regions compared to those without dementia. Interhemispheric asymmetry of cholinergic terminals correlated with asymmetry of brain metabolism and lateralized motor function. In conclusion, this study provides robust evidence for severe cholinergic terminal loss in newly diagnosed dementia with Lewy bodies, which correlates with structural imaging measures of cholinergic basal forebrain degeneration. In patients without dementia, our findings suggest that loss of cholinergic terminal function occurs 'before' neuronal cell degeneration. Moreover, the study supports that degeneration of the cholinergic system is important for brain metabolism and may be linked with degeneration in other transmitter systems. Our findings have implications for understanding how cholinergic system pathology contributes to the clinical features of Lewy body disease, changes in brain metabolism and disease progression patterns.

Increased tryptophan, but not increased glucose metabolism, predict resistance of pembrolizumab in stage III/IV melanoma

ABSTRACT Clinical trials of combined IDO/PD1 blockade in metastatic melanoma (MM) failed to show additional clinical benefit compared to PD1-alone inhibition. We reasoned that a tryptophan-metabolizing pathway other than the kynurenine one is essential. We immunohistochemically stained tissues along the nevus-to-MM progression pathway for tryptophan-metabolizing enzymes (TMEs; TPH1, TPH2, TDO2, IDO1) and the tryptophan transporter, LAT1. We assessed tryptophan and glucose metabolism by performing baseline C11-labeled α-methyl tryptophan (C11-AMT) and fluorodeoxyglucose (FDG) PET imaging of tumor lesions in a prospective clinical trial of pembrolizumab in MM (clinicaltrials.gov, NCT03089606). We found higher protein expression of all TMEs and LAT1 in melanoma cells than tumor-infiltrating lymphocytes (TILs) within MM tumors (n = 68). Melanoma cell-specific TPH1 and LAT1 expressions were significantly anti-correlated with TIL presence in MM. High melanoma cell-specific LAT1 and low IDO1 expression were associated with worse overall survival (OS) in MM. Exploratory optimal cutpoint survival analysis of pretreatment ‘high’ vs. ‘low’ C11-AMT SUVmax of the hottest tumor lesion per patient revealed that the ‘low’ C11-AMT SUVmax was associated with longer progression-free survival in our clinical trial (n = 26). We saw no such trends with pretreatment FDG PET SUVmax. Treatment of melanoma cell lines with telotristat, a TPH1 inhibitor, increased IDO expression and kynurenine production in addition to suppression of serotonin production. High melanoma tryptophan metabolism is a poor predictor of pembrolizumab response and an adverse prognostic factor. Serotoninergic but not kynurenine pathway activation may be significant. Melanoma cells outcompete adjacent TILs, eventually depriving the latter of an essential amino acid.

Abstract P2-03-17: Fluoroestradiol (FES) and Fluorodeoxyglucose (FDG) PET imaging in patients with ER+, HER2+ or HER2- metastatic breast cancer

Abstract Background: 18F-Fluorodeoxyglucose (FDG) has long been used for measuring tumor glycolytic activity in clinical PET imaging. The FDA recently approved 18F-Fluoroestradiol (FES) (Cerianna) as a PET imaging tracer for characterizing disease in patients with estrogen-receptor positive (ER+) breast cancer. As FES PET enters clinical practice it is important to establish its utility in the full population of hormone-receptor positive patients, including for patients with human epidermal growth factor 2 (HER2)-overexpressing tumors. Patients with HER2-positive metastatic breast cancer have historically been treated with combination cytotoxic and HER2-directed therapy, with the understanding that HER2 is the primary driver in this disease state. This retrospective study examined uptake in matched lesions for both FES and FDG PET and compared activity in patients with HER2 positive versus HER2 negative metastatic breast cancer. Methods: Patients were selected from the UW research database who had a history of biopsy-proven primary ER+ breast cancer as well as FES and FDG PET scans within 30 days. We examined FDG and FES scans and recorded SUVmax in up to 16 matched lesions between the two scans. Patients were also divided by HER2 status (+/-). In addition, a subset of patients who underwent at least 2 paired FDG and FES scans were reviewed. Results: 270 matched FDG and FES scans were analyzed in 216 patients with history of ER+, and HER2+ or HER2- breast cancer who were not part of an ongoing clinical trial. 158 (73%) had ductal disease, 38 (18%) had lobular disease. 183 (85%) had HER2- breast cancer. Of the 33 patients who had HER2+ breast cancer, 28 (85%) had ductal carcinoma. 40 patients underwent serial scans, allowing tracking over multiple timepoints. A total of 1323 metastatic sites were recorded (average = 5/scan (range 1,16)), with the majority (71%) representing bony lesions. No difference in quantitative FES or FDG avidity was observed between soft tissue and osseous sites. FES and FDG SUVmax were similar among patients with either HER2- or HER2+ breast cancer (Table 1). Among 40 patients with multiple paired FDG and FES scans, 26 (65%) had 2 scans while the remaining 14 had 3, with FES avidity remaining stable over time. There was no correlation between FES or FDG scans and HER2 status. Conclusion: In a cohort of ER+, HER2+ and HER2- patients undergoing concurrent FDG and FES PET scans, FDG and FES activity was similar regardless of HER2 status. FES uptake in both HER2- and HER2+ patients and stability over time in serial scans suggest that HER2 does not affect ER density. This suggests that in many patients with so-called “triple positive” disease, endocrine therapy may offer a powerful primary rather than ancillary tool in select patients. FES combined with FDG PET may offer utility in predicting and assessing response to therapy in this patient population. Table 1. FES and FDG uptake in patients with HER2- or HER2+ disease Citation Format: Natasha Hunter, Lanell M. Peterson, David A. Mankoff, Mark Muzi, Delphine Chen, William R. Gwin, Shaveta Vinayak, Nancy E Davidson, Jennifer M. Specht, Hannah Linden. Fluoroestradiol (FES) and Fluorodeoxyglucose (FDG) PET imaging in patients with ER+, HER2+ or HER2- metastatic breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-17.

Gene’s expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study

BackgroundPositron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa.MethodsmRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PSMA-1007 and [18F]F-FDG uptake kinetics in vitro. Confocal microscopy, immunofluorescence staining, and quantification analyses were performed to assess the intracellular and cellular membrane PSMA expression.ResultsML analyses identified a predictive functional network involving four glucose metabolism-related genes: ALDOB, CTH, PARP2, and SLC2A4. By contrast, FOLH1 expression (encoding for PSMA) did not provide any additive predictive value to the model. At a cellular level, the increase in proliferation rate and migratory potential by primary PCa cells was associated with enhanced FDG uptake and decreased PSMA retention (paralleled by the preferential intracellular localization).ConclusionsThe overexpression of a functional network involving four glucose metabolism-related genes identifies a higher risk of disease progression since the earliest phases of PCa, in agreement with the acknowledged prognostic value of FDG PET imaging. By contrast, the prognostic value of PSMA PET imaging is independent of the expression of its encoding gene FOLH1. Instead, it is influenced by the protein docking to the cell membrane, regulating its accessibility to tracer binding.

Associations between FDG metabolism and fMRI connectivity in the default mode network depend on Aβ status in MCI patients

AbstractBackgroundBrain function can be assessed using both resting‐state functional magnetic resonance imaging (rs‐fMRI) and Fluorodeoxyglucose (FDG) PET imaging. The associations between these biomarkers and their evolution with Alzheimer’s disease (AD) are unclear. We aimed to evaluate the associations between the default mode network (DMN) connectivity and glucose metabolism in mild cognitive impairment (MCI) patients with (Aβ+) and without (Aβ‐) AD pathology.MethodForty MCI patients (19 Aβ‐/21 Aβ+) performed between 2 to 4 rs‐fMRI acquisitions with an average follow‐up duration of 15 months. At baseline, DMN metabolism was estimated using [18F]‐FDG‐PET SUVr of the isthmus cingulate region‐of‐interest extracted from the Desikan‐Killiany atlas; and Aβ was measured using [18F]‐Flutemetamol‐PET. We used a Centiloid‐threshold&gt;26 to define Aβ+. Multiple regressions were used to assess the FDG‐fMRI associations in the DMN, comparing the two subgroups using FDG‐Aβ interaction models. Table 1 provides demographics and follow‐up durations. All analyses were adjusted for age and sex.ResultsIn the entire sample, DMN connectivity and metabolism were not significantly associated to each other. However, we observed an interaction between Aβ status and FDG metabolism (0.23, p=0.03) such that, in the high‐Aβ subgroup, lower FDG metabolism was signiticantly associated with lower DMN connectivity (Fig. 1). In the low‐Aβ subgroup, no associations were observed. We also observed an FDG‐Aβ interaction when predicting longitudinal changes in DMN (‐0.27, p=0.04) such that, in the high‐Aβ subgroup, lower FDG metabolism was associated with a subsequent increase in DMN connectivity over time (Fig. 2).ConclusionIn high‐Aβ MCI patients, DMN connectivity is positively associated with FDG metabolism measured at baseline. However, the longitudinal association is negative with lower metabolism predicting increasing connectivity over time. Future research is needed to understand the mechanisms behind this inverse relationship. Hypothetically, a synchronous decrease in metabolism in several regions of the DMN could lead to an increase in the correlation between resting‐state activity measured in these regions.

Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification

BackgroundDual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images. Methods[18F]-fluorodeoxyglucose ([18F]-FDG) PET images of 13 subjects were reconstructed with 4 methods: non-gated, dual-gated, motion corrected, and motion corrected with 4D-CT (MoCo-4D). Image quality was evaluated using standardized uptake values, contrast ratio, signal-to-noise ratio, coefficient of variation, and contrast-to-noise ratio. Motion minimization was evaluated using myocardial wall thickness. ResultsMoCo-4D showed improvement for contrast ratio (2.83 vs 2.76), signal-to-noise ratio (27.5 vs 20.3) and contrast-to-noise ratio (14.5 vs 11.1) compared to dual-gating. The uptake difference between MoCo-4D and non-gated images was non-significant (P > .05) for the myocardium (2.06 vs 2.15 g/mL), but significant (P < .05) for the blood pool (.80 vs .86 g/mL). Non-gated images had the lowest coefficient of variation (27.3%), with significant increase for all other methods (31.6-32.5%). MoCo-4D showed smallest myocardial wall thickness (16.6 mm) with significant decrease compared to non-gated images (20.9 mm). ConclusionsEnd-diastolic respiratory motion correction and 4D-CT resulted in improved motion minimization and image quality over standard dual-gating.

Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [18F]FDG PET imaging.

We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [18F]fluorodeoxyglucose (FDG) PET images. In this two-center study, 255 normal controls (NCs) and 103 PD patients were enrolled from Huashan Hospital, China; 26 NCs and 22 PD patients were enrolled as a separate test group from Wuxi 904 Hospital, China. The proposed DLR model consisted of a convolutional neural network-based feature encoder and a support vector machine (SVM) model-based classifier. The DLR model was trained and validated in the Huashan cohort and tested in the Wuxi cohort, and accuracy, sensitivity, specificity and receiver operator characteristic (ROC) curve graphs were used to describe the model's performance. Comparative experiments were performed based on four other models including the scale model, radiomics model, standard uptake value ratio (SUVR) model and DLR model. The DLR model demonstrated superiority in differentiating PD patients and NCs in comparison to other models, with an accuracy of 95.17% [90.35%, 98.13%] (95% confidence intervals, CI) in the Huashan cohort. Moreover, the DLR model also demonstrated greater performance in diagnosing PD early than routine methods, with an accuracy of 85.58% [78.60%, 91.57%] in the Huashan cohort. We developed a DLR model based on [18F]FDG PET images that showed good performance in the noninvasive, individualized prediction of PD and was superior to traditional handcrafted methods. This model has the potential to guide and facilitate clinical diagnosis and contribute to the development of precision treatment. The DLR method on [18F]FDG PET images helps clinicians to diagnose PD and PD subgroups from normal controls. A prospective two-center study showed that the DLR method provides greater diagnostic accuracy.

Editors' Note: Progressive Auditory Verbal Agnosia Secondary to Alzheimer Disease

In this issue of Neurology ®, Dr. Buciuc et al. describe the case of a patient with suspected Alzheimer disease and progressive auditory verbal agnosia. The clinical diagnosis is supported by nicely rendered [18F]-fluorodeoxyglucose PET imaging demonstrating hypometabolism in Heschl gyrus (primary auditory cortex), which corresponds with the patient's auditory verbal agnosia. The diagnosis of Alzheimer disease was reached on the basis of radiographic evidence of cortical atrophy with increased flortaucipir uptake in the bilateral temporal lobes (reflecting tau accumulation) and β-amyloid deposition on PET imaging, in the absence of other cognitive impairment. In response, Dr. Dasheiff commends the imaging findings and biologic explanation of this patient's condition; however, Dr. Dasheiff questions the diagnosis of Alzheimer disease given the lack of clinical pathology. The DSM-5 currently requires longitudinal follow-up and confirmation of progressive impairment in one or more cognitive domains to make the diagnosis of Alzheimer disease. The authors emphasize that their findings are consistent with Alzheimer pathology; however, they cannot confirm that an alternative tauopathy (or other clinical pathology) may account for this patient's neuroimaging and clinical manifestations. In this issue of Neurology ®, Dr. Buciuc et al. describe the case of a patient with suspected Alzheimer disease and progressive auditory verbal agnosia. The clinical diagnosis is supported by nicely rendered [18F]-fluorodeoxyglucose PET imaging demonstrating hypometabolism in Heschl gyrus (primary auditory cortex), which corresponds with the patient's auditory verbal agnosia. The diagnosis of Alzheimer disease was reached on the basis of radiographic evidence of cortical atrophy with increased flortaucipir uptake in the bilateral temporal lobes (reflecting tau accumulation) and β-amyloid deposition on PET imaging, in the absence of other cognitive impairment. In response, Dr. Dasheiff commends the imaging findings and biologic explanation of this patient's condition; however, Dr. Dasheiff questions the diagnosis of Alzheimer disease given the lack of clinical pathology. The DSM-5 currently requires longitudinal follow-up and confirmation of progressive impairment in one or more cognitive domains to make the diagnosis of Alzheimer disease. The authors emphasize that their findings are consistent with Alzheimer pathology; however, they cannot confirm that an alternative tauopathy (or other clinical pathology) may account for this patient's neuroimaging and clinical manifestations.

Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions

BackgroundPositron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm–16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated.ResultsThe 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM’s PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined “apex”), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions le 6 mm in diameter (R2 = 0.979–0.996 vs. R2 = 0.115–0.527, respectively).ConclusionAn anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200–400 and OSEM with 2–5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

Genotype-phenotype of PSEN1 p.CYS263PHE carriers in Flanders-Belgian Alzheimer's disease patients.

We identified 13 unrelated index patients carrying the presenilin 1 (PSEN1) missense mutation, p.Cys263Phe in a Flanders-Belgian cohort of Alzheimer's disease (AD) patients. Seven affected relatives in three different families also carried the PSEN1 p.Cys263Phe mutation (n=20). We aimed to delineate a clinicopathological phenotype of p.Cys263Phe carriers. We compared the p.Cys263Phe genotype-phenotype with that of AD patients carrying autosomal dominant mutations i.e. PSEN1 (n=29), PSEN2 (n=1), and APP (n=5). Reviewing of medical records of autosomal dominant mutation carriers to obtain clinical and pathological data for defining genotype-phenotype data. Mean onset age of the p.Cys263Phe carriers is 63.9±5.5 years (range 53-79), with a disease duration of 7.0±3.5 years (range 4-13). APOE genotypes have no significant effects on onset age. A positive familial history is present in 92.9% of the carriers. Segregation with disease is present in two families with autosomal dominant inheritance of AD (figure 1). In all carriers the clinical presentation is predominantly amnestic, although 35.7% (5/14) of the patients also show significant frontal symptoms. Structural neuroimaging displays diffuse (sub)cortical atrophy with evident hippocampal atrophy in 26.7% (4/15) of the carriers. In 40.0% (6/15) of the patients we observe severe signs of small vessel disease. Brain perfusion SPECT or fluorodeoxyglucose PET imaging shows bilateral temporoparietal involvement in 57.1% (4/7) of carriers. Cerebrospinal fluid AD biomarkers were analyzed in eight carriers, and all are characteristic for AD. Neuropathological examination in three carriers shows severe levels of hallmark AD lesions combined with pronounced cerebral amyloid angiopathy (CAA) and multiple intracerebral hemorrhages in one patient. Carriers of p.Cys263Phe have a later age at onset (63.9 years) compared to PSEN1 carriers (50.9 years) and other PSEN1, PSEN2 and APP mutation carriers (51.4 years) (figure 2). PSEN1 p.Cys263Phe carriers present with early-onset familial AD with autosomal dominant co-segregation. Important is that frontal symptoms are seen in 35.7% of the carrier while neuropathological examination reveals severe levels of AD neuropathology with prominent presence of CAA. The disease onset of PSEN1 p.Cys263Phe carriers is later in comparison with other autosomal dominant gene mutation carriers.

Characterizing the hyper- and hypometabolism in temporal lobe epilepsy using multivariate machine learning.

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole-brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [18 F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region-of-interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo- and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

High Correlation of Static First-Minute-Frame (FMF) PET Imaging after 18F-Labeled Amyloid Tracer Injection with [18F]FDG PET Imaging.

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson’s correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.

Classification of Myocardial 18F-FDG PET Uptake Patterns Using Deep Learning.

To perform automated myocardial segmentation and uptake classification from whole-body fluorine 18 fluorodeoxyglucose (FDG) PET. In this retrospective study, consecutive patients who underwent FDG PET imaging for oncologic indications were included (July-August 2018). The left ventricle (LV) on whole-body FDG PET images was manually segmented and classified as showing no myocardial uptake, diffuse uptake, or partial uptake. A total of 609 patients (mean age, 64 years ± 14 [standard deviation]; 309 women) were included and split between training (60%, 365 patients), validation (20%, 122 patients), and testing (20%, 122 patients) datasets. Two sequential neural networks were developed to automatically segment the LV and classify the myocardial uptake pattern using segmentation and classification training data provided by human experts. Linear regression was performed to correlate findings from human experts and deep learning. Classification performance was evaluated using receiver operating characteristic (ROC) analysis. There was moderate agreement of uptake pattern between experts and deep learning (as a fraction of correctly categorized images) with 78% (36 of 46) for no uptake, 71% (34 of 48) for diffuse uptake, and 71% (20 of 28) for partial uptake. There was no bias in LV volume for partial or diffuse uptake categories (P = .56); however, deep learning underestimated LV volumes in the no uptake category. There was good correlation for LV volume (R 2 = 0.35, b = .71). ROC analysis showed the area under the curve for classifying no uptake and diffuse uptake was high (> 0.90) but lower for partial uptake (0.77). The feasibility of a myocardial uptake index (MUI) for quantifying the degree of myocardial activity patterns was shown, and there was excellent visual agreement between MUI and uptake patterns. Deep learning was able to segment and classify myocardial uptake patterns on FDG PET images.Keywords: PET, Heart, Computer Aided Diagnosis, Computer Application-Detection/DiagnosisSupplemental material is available for this article.©RSNA, 2021.