FDG Positron Emission Tomography Imaging Research Articles

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

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

\u201cString of pearls sign\u201d on FDG PET imaging in two patients with PUO of diverse etiologies and rare associations

BackgroundPyrexia of unknown origin (PUO) may be related to several pathologies that need to be identified for proper treatment. PET is found to have highest diagnostic yield in identifying various causes of PUO. The aim of this study is to highlight and justify the use of 18F FDG PET (Fluorine Fluorodeoxyglucose Positron Emission Tomography) imaging as a whole body screening tool in two unique cases of febrile illness with lymphadenopathy but with diverse etiologies based on PET-guided biopsy. The unique arrangement of PET positive nodal disease as a “string of pearls sign” helps in easy identification of nodal disease.Case presentationThe first case presented with fever and headache (past history of meningitis), high ferritin level, leukopenia, anemia, and raised inflammatory markers. CSF cell count was high, with mildly elevated protein and low glucose levels. PET positive nodes were biopsied; Kikuchi Fujimoto disease was confirmed with unexpected FDG avid pachymeningeal enhancements on PETMR indicating associated (active) meningitis which would have been missed if CT or MR was done as a standalone imaging. Lumbar puncture confirmed aseptic meningitis. The patient was treated with anti-inflammatory drugs, inj. methylprednisolone, and immunoglobulin together with hydroxychloroquine. The patient improved on follow-up. The second case presented with low-grade fever, pruritis, and nodular swellings in extremities, anemia, and pancytopenia. Based on PETCT findings, biopsy was attempted from FDG avid axillary nodes. Mantle cell lymphoma with rare nodular involvement of cutaneous and subcutaneous tissue was confirmed. Chemotherapy and tyrosine kinase inhibitor therapy was initiated, and the patient is doing well clinically.ConclusionThe clinical impact of PET is twofold in both cases. It has accurately identified the nodal involvement even those subcentimetric in size by exhibiting a unique sign on PET resembling a “string of pearls” in the neck and chest with unrelated diverse etiologies. Secondly, additional findings of meningitis in the first case and cutaneous/subcutaneous nodular lymphomatous deposits in the second patient were possible only with whole body FDG PETMR/CT imaging. The rate of additional disease detection by PET is found to be greater than other conventional imaging modalities due to the functional basis of investigation.

Estimation of the net influx rate Ki and the cerebral metabolic rate of glucose MRglc using a single static [18F]FDG PET scan in rats

Since accurate quantification of 2-deoxy-2–18F-fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) requires dynamic acquisition with arterial input function, more practical semi-quantitative (static) approaches are often preferred. However, static standardized uptake values (SUV) are typically biased due to large variations in body weight (BW) occurring over time in animal studies. This study aims to improve static [18F]FDG PET SUV quantification by better accounting for BW variations in rats. We performed dynamic [18F]FDG PET imaging with arterial blood sampling in rats (n = 27) with different BW (range 0.230–0.487 kg). By regressing the area under the curve of the input function divided by injected activity against BW (r2=0.697), we determined a conversion factor f(BW) to be multiplied with SUV and SUVglc to obtain ratSUV and ratSUVglc, providing an improved estimate of the net influx rate Ki (r = 0.758, p<0.0001) and cerebral metabolic rate of glucose MRglc (r = 0.906, p<0.0001), respectively. In conclusion, the proposed ratSUV and ratSUVglc provide a proxy for the Ki and MRglc based on a single static [18F]FDG PET SUV measurement improving clinical significance and translation of rodent studies. Given a defined strain, sex, age, diet, and weight range, this method is applicable for future experiments by converting SUV with the derived f(BW).

HRD1 attenuates the high uptake of [18F]FDG in hepatocellular carcinoma PET imaging

IntroductionDue to individual deviations in tumor tissue uptake, the role of [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) in hepatocellular carcinoma (HCC) diagnosis is limited. β-Hydroxy β-methylglutaryl-CoA reductase degradation 1 (HRD1) plays a key role in clearing misfolded proteins. This study is aimed to investigate the role and mechanism of HRD1 in [18F]FDG uptake for the diagnosis of HCC. MethodsHRD1 expression level was detected using immunohistochemical (IHC) staining in 9 HCC patients. [18F]FDG PET/CT scans were conducted before treatment. [18F]FDG uptakes in HRD1 overexpressed and knockdown transgenic models were measured by γ-counter and microPET imaging. The GLUT1-HRD1 complex was examined by co-immunoprecipitation and IHC assays. GLUT1 expression in different cell lines, xenograft models and HCC patients was evaluated by Western blot and IHC assays. ResultsHRD1 was highly expressed in the HCC tumors of patients with low [18F]FDG uptake, while the HRD1 expression was obviously low in the higher [18F]FDG uptake group. Both in vitro and in vivo studies found that HRD1 significantly inhibited [18F]FDG uptake in HCC Huh7 cell lines and animal models. Furthermore, the co-location and interaction of HRD1 with GLUT1 were detected, and the results also indicate that HRD1 could induce the degradation of GLUT1 in vitro and in vivo. ConclusionHRD1 inhibits the high uptake of [18F]FDG in HCC tumor cells by inducing degradation of GLUT1, which leads to decreased diagnostic efficiency of [18F]FDG PET imaging for HCC. Advances in knowledgeThis study suggests that HRD1 inhibits the high uptake of [18F]FDG in HCC tumor by inducing degradation of GLUT1. Implications for patient careHCC diagnosis with [18F]FDG PET should be accompanied by determination of HRD1 expression, and patients with high tumor HRD1 expression might be unsuitable for [18F]FDG PET.

Effect of uncertainties in quantitative 18 F-FDG PET/CT imaging feedback for intratumoral dose-response assessment and dose painting by number.

Intratumoral dose response can be detected using serial fluoro-2-deoxyglucose-(FDG) positron emission tomography (PET)/computed tomography (CT) imaging feedback during treatment and used to guide adaptive dose painting by number (DPbN). However, to reliably implement this technique, the effect of uncertainties in quantitative PET/CT imaging feedback on tumor voxel dose-response assessment and DPbN needs to be determined and reduced. Three major uncertainties, induced by (a) PET imaging partial volume effect (PVE) and (b) tumor deformable image registration (DIR), and (c) variation of the time interval between FDG injection and PET image acquisition (TI), were determined using serial FDG-PET/CT images acquired during chemoradiotherapy of 18 head and neck cancer patients. PET imaging PVE was simulated using the discrepancy between with and without iterative deconvolution-based PVE corrections. Effect of tumor DIR uncertainty was simulated using the discrepancy between two DIR algorithms, including one with and one without soft-tissue mechanical correction for the voxel displacement. The effect of TI variation was simulated using linear interpolation on the dual-point PET/CT images. Tumor voxel pretreatment metabolic activity (SUV0 ) and dose-response matrix (DRM) discrepancies induced by each of the three uncertainties were quantified, respectively. Adverse effects of tumor voxel SUV0 and DRM discrepancies on tumor control probability (TCP) in DPbN were assessed. Partial volume effect and TI variations of 10 mins induced a mean±standard deviation (SD) of tumor voxel SUV0 discrepancies to be -0.7%±9.2% and 0%±4.8%, respectively. Tumor voxel DRM discrepancies induced by PVE, tumor DIR discrepancy, and TI variations were 0.6%±8.9%, 1.7%±9.1%, and 0%±7%, respectively. Partial volume effect induced SUV0 and DRM discrepancies correlated significantly with the tumor shape and FDG uptake heterogeneity. Tumor DIR uncertainty-induced DRM discrepancy correlated significantly with the tumor volume and shrinkage during treatment. Among the three uncertainties, PVE dominated the adverse effects on the TCP, with a mean±SD of TCP reduction to be 12.7%±9.8% for all tumors if no compensation was applied for. Effect of uncertainties in quantitative FDG-PET/CT imaging feedback on intratumoral dose-response quantification was not negligible. These uncertainties primarily caused by PVE and tumor DIR were highly dependent on individual tumor shape, volume, shrinkage during treatment, and pretreatment SUV heterogeneity, which can be managed individually. The adverse effects of these uncertainties could be minimized by using proper PVE corrections and DIR methods and compensated for in the clinical implementation of DPbN.

Differential Diagnosis of Parkinsonism with Deep Metabolic Imaging Biomarker – An Artificial Intelligence-Aided Multi-Cohort FDG PET Study

Background: The clinical presentation of early idiopathic Parkinson’s disease (IPD) substantially overlaps with that of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has proven useful in assisting in the differential diagnosis of these parkinsonian syndromes. The aim of this study was to develop a metabolic imaging biomarker based on deep learning to support the differential diagnosis of parkinsonism. Methods: In addition to 863 non-parkinsonian subjects, a total of 1275 parkinsonian patients were accessed who underwent FDG PET imaging, clinical evaluation, and follow-up, yielding clinically possible, definite, or confirmative diagnoses of IPD, MSA, and PSP. A 3D deep convolutional neural network was developed to extract a deep metabolic imaging (DMI) biomarker. The accuracy of the DMI biomarker was evaluated with a cross-validation and a blind test. The robustness of the DMI biomarker was assessed on patients with longitudinal follow-up. The functional basis of DMI biomarker was further investigated based on the saliency map of the obtained deep neural network. Findings: The proposed DMI biomarker achieved an area under the receiver operating characteristic curve of 0·986 for the differential diagnosis of IPD, 0·997 for MSA, and 0·982 for PSP in the cross-validation. In the blind test, the DMI biomarker resulted in sensitivities of 98·1%, 88·5%, and 84·5%, as well as specificities of 90·0%, 99·2%, and 97·8% for IPD, MSA, and PSP respectively. The integration of demographic information and clinical assessments to DMI biomarker resulted in slight improvements. Saliencies for the DMI biomarker were found in several parkinsonism-related regions such as midbrain, putamen, and cerebellum. Interpretation: The DMI biomarker based on deep learning shows potential to provide accurate differential diagnosis for parkinsonism on FDG PET. The functional regions behind the DMI biomarker are consistent with known parkinsonian pathology. Funding Statement: The work was supported by grants from the National Natural Science Foundation of China (No. 81771483, 81671239, 81361120393, 81401135, 81971641, 81902282, 91949118, 81771372), from the Ministry of Science and Technology of China (2016YFC1306504), Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01, 2018SHZDZX03) and ZJ Lab, Shanghai Sailing Program (No. 18YF1403100). It was also supported by Swiss National Science Foundation (No. 188350) and Siemens Healthineers. Declaration of Interests: W.H.O is Hertie Senior Research Professor, supported by the Charitable Hertie Foundation, Frankfurt/Main, Germany. A.R. and K.S. received research support from Siemens Healthineer. Other authors report no financial interests or potential conflicts of interest regarding the subject matter of the manuscript. Ethics Approval Statement: All procedures performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All the data were from Parkinson's Disease Database and Samples Bank in Huashan Hospital. Ethics permission was obtained from the Institutional Review Board of Huashan Hospital and written consent was obtained from each subject after detailed explanation of the procedures.

Semi-quantitative analysis of 18F fluorodeoxyglucose uptake in the assessment of disease activity and therapeutic response in rheumatoid arthritis: An institutional experience.

18F fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) can be used to image synovial inflammation in patients with rheumatoid arthritis (RA). Recently, clinical application of novel therapies for RA, such as tumor necrosis factor-α (TNF-α) inhibitor and anti-interleukin-6 receptor antibody, has been introduced. The radiological assessment of disease activity changes of patients who underwent these therapies will help the clinicians to obtain more information about the patients and to decide drug withdrawal or change of medication. It is considered that 18F-FDG PET scan is generally very expensive; however, the information from 18F-FDG PET about patients during biological treatments helps discontinuation of these treatments with incomplete response despite its high costs and with possible side effects such as malignant lymphoma. In this study, we evaluated if the 18F-FDG uptake of the affected joints represented by standardized uptake value (SUV) correlated with the clinical assessment of patients with RA. In addition, we would like to evaluate if there was a correlation between the difference of SUV and improvement of clinical findings in RA patients undergoing anti TNF therapies. RA patients who underwent anti-TNFα therapies in a tertiary care hospital were assessed using whole-body 18F-FDG PET/computed tomography (CT). PET assessments were performed on hip joints, knees, shoulders, wrists, ankles, MCP, and PIP for a total of 28 joints in each patient. The 18F–FDG uptake was then quantified using the maximum SUV (SUVmax) prior to, and 6 months after the initiation of treatment with anti-TNF-α drugs. Disease activity score (DAS28 and DAS28-C-reactive protein [CRP]) were recorded and white blood cell, matrix metalloproteinases (MMP-3) and rheumatoid factor (RF) were examined in all patients. The average of SUVmax among measured joints, or the sum of these joints (total SUVmax), correlated with DAS28 (r = 0.671, P < 0.001), DAS28-CRP (r = 0.623, P < 0.001), ESR (r = 0.542, P < 0.001), CRP (r = 0.411, P = 0.002), MMP-3 (r = 0.399, P = 0.006), and RF (r = 0.447, P = 0.002). There were correlations between ΔSUV and ΔDAS28 (r = 0.651, P < 0.001), ΔSUV and ΔDAS28-CRP (r = 0.682, P < 0.001), ΔSUV and ΔESR (r = 0.449, P = 0.023), and ΔSUV and ΔMMP-3 (r = 0.457, P = 0.027), respectively. The number of PET-positive joints and the cumulative SUV significantly correlated with the DAS28, which is a composite disease activity score (DAS) that combines the swollen and tender joint counts, the erythrocyte sedimentation rate (DAS28-ESR) or CRP serum levels (DAS28 − CRP) or RF (DAS28 − RF) or metalloproteinases-3 (DAS28-MMP-3). At baseline and at 6 months' post-treatment with anti-TNFα drugs, there was a significant correlation between the PET results, either visual, the cumulative SUVs or the composite SUV index, and the comprehensive clinical assessment (DAS28), the CRP levels and the number of joints positive for RA, and cumulative synovial thickness. By reflecting inflammatory activity, 18F-FDG PET may enhance the diagnostic performance and expectation of disease prognosis in RA, especially with early synovial inflammation. The intensity of uptake varied from mild to intense (SUVmax values from 3.10 to 6.0). Overall, these values correlated well with the clinical evaluation of involved joints. 18F–FDG PET imaging data provided a distribution of joint involvement with varying degrees of severity and phase of disease activity (moderate, low, and remission) in the same patient. PET/CT imaging with 18F-FDG shows better image quality, provides more confirmative diagnostic information, and will be promising imaging modality in diagnosis and management of RA.

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review.

Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented methodology used for texture analysis and lack of statistical robustness, at present, no firm conclusions can be drawn regarding the predictive or prognostic value of FDG PET texture analysis derived indices in patients suffering from gastro-enterologic tumors. In order to move forward in this field, a harmonized image acquisition and processing protocol as well as a harmonized protocol for texture analysis of tumor volumes, allowing multi-center studies excluding statistical biases should be considered. Furthermore, the complementary and additional value of CT-imaging, as part of the PET/CT imaging technique, warrants exploration.

Abstract P1-19-22: Pearl or the use of FDG positron emission tomography imaging as an early dynamic biomarker of everolimus efficacy in advanced luminal breast cancer

Abstract Background: The combination of everolimus and an endocrine agent has shown a progression-free survival (PFS) benefit but also significant toxicity in locally advanced and/or metastatic breast cancer (MBC) patients. Disappointingly, all translational research efforts conducted so far on primary/metastatic tissue or baseline ctDNA samples have failed to identify clinically useful biomarkers of benefit with the possible exception of p4EBP1 which requires fresh metastatic biopsies (Bachelot at al, ASCO 2019). Methods: Forty-seven patients were included in the PEARL trial in six Belgian centres. All patients underwent one baseline and one “early” 18FDG-PET/CT on day 14 (blinded to the treating oncologist) after treatment initiation. Eligible patients had prior progression on NSAI and at least one measurable lesion on baseline 18FDG-PET/CT (fulfilling PERCIST). Twenty seven of the 47 patients were treated in a pilot phase aiming to characterize 18FDG-PET/CT metabolic responses 14 and 28 days after treatment initiation. Day 14 18FDG-PET/CT was selected for the second phase in which 20 more patients were included to answer the primary endpoint of the trial, namely the potential of an early FDG-PET/CT to select patients who will not derive a long-term benefit from everolimus-exemestane combination. We considered as “metabolic responders” those patients in whom at least a 25% homogenous decrease in SUVmax was seen in all their target lesions; the so called “consistent” patient-based response, here correlated to PFS. Results: Seventy-four percent of the patients had visceral metastasis, 26% were pretreated with CDK 4/6 inhibitors and 58% with at least one line of chemotherapy (28% ≥ 2 lines) for the treatment of MBC. Metabolic non-response rate on day 14 was 64%. Two patients were considered non-evaluable because they stopped everolimus 3 and 8 days before early 18FDG-PET/CT respectively. The median PFS in non-responding patients was 2.3 months compared to 6 months in those who showed a consistent metabolic response (HR-1.96, 95%CI: 0.98-3.95, p=0.05). We’ll be able to assess whether day 14 ctDNA measurements add predictive value to imaging. Conclusion: Absence of 18FDG-PET/CT metabolic response after 14 days of everolimus-exemestane combination is associated with a low probability to derive benefit from this treatment. The advanced stage of the patients included in this trial (75% visceral metastasis) and their extensive prior treatments can explain the poorer outcome seen with exemestane-everolimus compared to the Bolero2 trial. Citation Format: Andrea Gombos, Martine Piccart-Gebhart, Lieveke Ameye, Peter Vuylsteke, Patrick Neven, Vincent Richard, Francois P Duhoux, Marianne Paesmans, Michail Ignatiadis, Patrick Flamen, Géraldine Gebhart. Pearl or the use of FDG positron emission tomography imaging as an early dynamic biomarker of everolimus efficacy in advanced luminal breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-19-22.

The risk prediction of Alzheimer's disease based on the deep learning model of brain 18F-FDG positron emission tomography.

In order to predict the risks of Alzheimer’s Disease (AD) based on the deep learning model of brain 18F-FDG positron emission tomography (PET), a total of 350 mild cognitive impairment (MCI) participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were selected as the research objects; in addition, the Convolutional Architecture for Fast Feature Embedding (CAFFE) was selected as the framework of the deep learning platform; the FDG PET image features of each participant were extracted by a deep convolution network model to construct the prediction and classification models; therefore, the MCI stage features were classified and the transformation was predicted. The results showed that in terms of the MCI transformation prediction, the sensitivity and specificity of conv3 classification were respectively 91.02% and 77.63%; in terms of the Late Mild Cognitive Impairment (LMCI) and Early Mild Cognitive Impairment (EMCI) classification, the accuracy of conv5 classification was 72.19%, and the sensitivity and specificity of conv5 were all 73% approximately. Thus, it was seen that the model constructed in the research could be used to solve the problems of MCI transformation prediction, which also had certain effects on the classifications of EMCI and LMCI. The risk prediction of AD based on the deep learning model of brain 18F-FDG PET discussed in the research matched the expected results. It provided a relatively accurate reference model for the prediction of AD. Despite the deficiencies of the research process, the research results have provided certain references and guidance for the future exploration of accurate AD prediction model; therefore, the research is of great significance.

Noninvasive Classification of Human Triple Negative Breast Cancer by PET Imaging with GRP78-Targeted Molecular Probe [68Ga]DOTA-VAP.

There is currently no effective noninvasive method for accurate molecular typing of triple negative breast cancer (TNBC) except needle biopsy. Glucoregulated Protein 78 (GRP78) is overexpressed in TNBC cells and tumors which closely related to the invasion, metastasis, and drug resistance of cancer. Meanwhile, it has been verified that VAP peptide bind specifically to GRP78 in vitro and in vivo. In this study, we constructed a GRP78-targeted molecular probe Ga-68-radiolabeled DOTA-VAP conjugate ([68Ga]DOTA-VAP) based on VAP peptide, and evaluated its potential to distinguish TNBC from non-TNBC tumors. DOTA-VAP was synthesized and then radiolabeled with Ga-68 to obtain [68Ga]DOTA-VAP. The expression of GRP78 in TNBC MDA-MB-231 and non-TNBC MCF-7 cells was validated by Western Blot, and cell binding or uptake experiments with both [68Ga]DOTA-VAP and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) were also performed. Biodistribution analysis and positron emission tomography (PET) imaging of [68Ga]DOTA-VAP were carried out in subcutaneous MDA-MB-231 and MCF-7 human breast cancer tumor models with [18F]FDG PET imaging as comparison. [68Ga]DOTA-VAP was prepared with high radiochemical purity which showed excellent stability in vitro. The MDA-MB-231 tumors were clearly visualized by [68Ga]DOTA-VAP PET imaging with a low background, except for the relatively high liver uptake. Cells and tumors of MDA-MB-231 could be distinguished from MCF-7 by [68Ga]DOTA-VAP instead of [18F]FDG. Biodistribution results were consistent with the imaging results. The blocking study with excess cold peptide showed significantly reduced tumor uptake, which indicated the specificity of [68Ga]DOTA-VAP targeting MDA-MB-231 tumors in vivo. GRP78-targeted PET imaging with [68Ga]DOTA-VAP provided an effective approach for the noninvasive accurate classification of TNBC from other breast cancer subtypes comparing with [18F]FDG. GRP78 may be a potential target for the diagnosis and treatment of TNBC. For clinical transformation, efforts should be made to overcome deficiencies of [68Ga]DOTA-VAP such as relative high uptake in normal tissues.

Repeatability of brown adipose tissue measurements on FDG PET/CT following a simple cooling procedure for BAT activation.

Brown Adipose Tissue (BAT) is present in a significant number of adult humans and can be activated by exposure to cold. Measurement of active BAT presence, activity, and volume are desirable for determining the efficacy of potential treatments intended to activate BAT. The repeatability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) measurements of BAT presence, activity, and volume under controlled conditions has not been extensively studied. Eleven female volunteers underwent double baseline FDG PET imaging performed following a simple, regional cold intervention intended to activate brown fat. The cold intervention involved the lightly-clothed participants intermittently placing their feet on a block of ice while sitting in a cooled room. A repeat study was performed under the same conditions within a target of two weeks. FDG scans were obtained and maximum standardized uptake value adjusted for lean body mass (SULmax), CT Hounsfield units (HU), BAT metabolic volume (BMV), and total BAT glycolysis (TBG) were determined according to the Brown Adipose Reporting Criteria in Imaging STudies (BARCIST) 1.0. A Lin’s concordance correlation (CCC) of 0.80 was found for BMV between test and retest imaging. Intersession BAT SULmax was significantly correlated (r = 0.54; p < 0.05). The session #1 mean SULmax of 4.92 ± 4.49 g/mL was not significantly different from that of session #2 with a mean SULmax of 7.19 ± 7.34 g/mL (p = 0.16). BAT SULmax was highly correlated with BMV in test and retest studies (r ≥ 0.96, p < 0.001). Using a simplified ice-block cooling method, BAT was activated in the majority (9/11) of a group of young, lean female participants. Quantitative assessments of BAT SUL and BMV were not substantially different between test and retest imaging, but individual BMV could vary considerably. Intrasession BMV and SULmax were strongly correlated. The variability in estimates of BAT activity and volume on test-retest with FDG should inform sample size choice in studies quantifying BAT physiology and support the dynamic metabolic characteristics of this tissue. A more sophisticated cooling method potentially may reduce variations in test-retest BAT studies.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 ×1 ×1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Current Radiopharmaceuticals for Positron Emission Tomography of Brain Tumors.

Brain tumors represent a diverse spectrum of histology, biology, prognosis, and treatment options. Although MRI remains the gold standard for morphological tumor characterization, positron emission tomography (PET) can play a critical role in evaluating disease status. This article focuses on the use of PET with radiolabeled glucose and amino acid analogs to aid in the diagnosis of tumors and differentiate between recurrent tumors and radiation necrosis. The most widely used tracer is 18F-fluorodeoxyglucose (FDG). Although the intensity of FDG uptake is clearly associated with tumor grade, the exact role of FDG PET imaging remains debatable. Additionally, high uptake of FDG in normal grey matter limits its use in some low-grade tumors that may not be visualized. Because of their potential to overcome the limitation of FDG PET of brain tumors, 11C-methionine and 18F-3,4-dihydroxyphenylalanine (FDOPA) have been proposed. Low accumulation of amino acid tracers in normal brains allows the detection of low-grade gliomas and facilitates more precise tumor delineation. These amino acid tracers have higher sensitivity and specificity for detecting brain tumors and differentiating recurrent tumors from post-therapeutic changes. FDG and amino acid tracers may be complementary, and both may be required for assessment of an individual patient. Additional tracers for brain tumor imaging are currently under development. Combinations of different tracers might provide more in-depth information about tumor characteristics, and current limitations may thus be overcome in the near future. PET with various tracers including FDG, 11C-methionine, and FDOPA has improved the management of patients with brain tumors. To evaluate the exact value of PET, however, additional prospective large sample studies are needed.

PET Imaging on Dynamic Metabolic Changes after Combination Therapy of Paclitaxel and the Traditional Chinese Medicine in Breast Cancer-Bearing Mice.

The aim of the study was to non-invasively evaluate the anticancer activity of a traditional Chinese medicine-Huaier, combined with paclitaxel (PTX) in breast cancer bearing mice by detecting dynamic metabolic changes with positron emission tomography (PET). Balb/c nude mice were randomly divided into one of the four groups: Huaier, PTX, PTX+Huaier, or the control. PET imaging with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) was performed to monitor the metabolic changes in BT474 (luminal B) and MDA-MB-231 (triple-negative) breast cancer xenografts. Immunohistochemistry (IHC) study was performed immediately after the final PET scan to assess the expressions of phosphatidylinositol 3-kinase (PI3K), phospho-AKT (p-AKT), caspase-3, and vascular endothelial growth factor (VEGF). Compared to the control group, [18F]FDG accumulation demonstrated a significant decrease in PTX+Huaier (p<0.01) or Huaier group (p<0.05), which was consistent to the decreased expression of PI3K (p<0.05) and p-AKT (p<0.05) in the breast cancer xenografts. The therapeutic effect of Huaier combined with PTX was superior than the PTX alone in BT474 and MDA-MB-231 breast cancer-bearing mice. [18F]FDG PET imaging could be a potential non-invasive approach to assess the metabolic changes after chemotherapy combined with traditional Chinese medicine in the breast cancer.

Visualization of Synthetic Vascular Smooth Muscle Cells in Atherosclerotic Carotid Rat Arteries by F-18 FDG PET

Synthetic vascular smooth muscle cells (VSMCs) play important roles in atherosclerosis, in-stent restenosis, and transplant vasculopathy. We investigated the synthetic activity of VSMCs in the atherosclerotic carotid artery using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Atherosclerosis was induced in rats by partial ligation of the right carotid artery coupled with an atherogenic diet and vitamin D injections (2 consecutive days, 600,000 IU/day). One month later, rats were imaged by F-18 FDG PET. The atherosclerotic right carotid arteries showed prominent luminal narrowing with neointimal hyperplasia. The regions with neointimal hyperplasia were composed of α-smooth muscle actin-positive cells with decreased expression of smooth muscle myosin heavy chain. Surrogate markers of synthetic VSMCs such as collagen type III, cyclophilin A, and matrix metallopeptidase-9 were increased in neointima region. However, neither macrophages nor neutrophils were observed in regions with neointimal hyperplasia. F-18 FDG PET imaging and autoradiography showed elevated FDG uptake into the atherosclerotic carotid artery. The inner vessel layer showed higher tracer uptake than the outer layer. Consistently, the expression of glucose transporter 1 was highly increased in neointima. The present results indicate that F-18 FDG PET may be a useful tool for evaluating synthetic activities of VSMCs in vascular remodeling disorders.

Reproducibility of 18F-fluoromisonidazole intratumour distribution in non-small cell lung cancer.

BackgroundHypoxic tumours exhibit increased resistance to radiation, chemical, and immune therapies. 18F-fluoromisonidazole (FMISO) positron emission tomography (PET) is a non-invasive, quantitative imaging technique used to evaluate the presence and spatial distribution of tumour hypoxia. To facilitate the use of FMISO PET for identification of individuals likely to benefit from hypoxia-targeted treatments, we investigated the reproducibility of FMISO PET spatiotemporal intratumour distribution in patients with non-small cell lung cancer (NSCLC).MethodsTen patients underwent 18F-fluorodeoxyglucose (FDG) PET/CT scans, followed by two FMISO PET/CT scans 1–2 days apart. Nineteen lesions in total were segmented from co-registered FDG PET image sets. Volumes of interest were also defined on normal contralateral lung and subscapularis muscle. The Pearson correlation coefficient r was calculated for mean standardized uptake values (SUV) within investigated volumes of interest and for voxels within tumour volumes (r TV). The reproducibility of FMISO voxelwise distribution, SUV- and tumour-to-blood ratio (TBR)-derived indices was assessed using correlation and Bland-Altman analyses.ResultsThe SUVmax, SUVmean, TBRmax, and TBRmean were highly correlated (r ≥ 0.87, p < 0.001) and were reproducible to within 10–15 %. The mean r TV was 0.84 ± 0.10. 77 % of voxels identified as hypoxic on one FMISO scan were confirmed as such on the other FMISO scan. Mean voxelwise differences between TBR values as calculated from pooled data including all lesions were 0.9 ± 10.8 %.ConclusionsHigh reproducibility of FMISO intratumour distribution in NSCLC patients was observed, facilitating its use in determining the topology of the hypoxic tumour sub-volumes for dose escalation, in patient stratification strategies for hypoxia-targeted therapies, and in monitoring response to therapeutic interventions.Trial registrationCurrent Controlled Trials NCT02016872

Prognostic Value of Nodal SUVmax of Pretreatment FDG Positron Emission Tomography Imaging in Patients With Esophageal Cancer

Prognostic Value of Nodal SUVmax of Pretreatment FDG Positron Emission Tomography Imaging in Patients With Esophageal Cancer

Intratumor Partitioning of Serial Computed Tomography and FDG Positron Emission Tomography Images Identifies High-Risk Tumor Subregions and Predicts Patterns of Failure in Non-Small Cell Lung Cancer After Radiation Therapy

Intratumor Partitioning of Serial Computed Tomography and FDG Positron Emission Tomography Images Identifies High-Risk Tumor Subregions and Predicts Patterns of Failure in Non-Small Cell Lung Cancer After Radiation Therapy

Interim FDG Positron Emission Tomography Imaging During Chemoradiation for Squamous Anal Canal Carcinoma

Interim FDG Positron Emission Tomography Imaging During Chemoradiation for Squamous Anal Canal Carcinoma