18F-FDG PET Imaging Research Articles

Role of FDG-PET CT in differentiating adenocarcinomas from squamous cell carcinomas of the lung

Abstract Background Lung cancer is the leading cause of cancer related deaths worldwide. The combination of fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) has a great impact on the diagnosis, staging, and hence on the treatment plan and follow up of lung cancer patients. Maximum standardized uptake value (SUVmax) is a semi-quantitative index which is easily performed and the most widely used quantitative parameter for the analysis of 18F-FDG PET images and for the estimation of metabolic activity. Objective to investigate the role of PET/ CT to differentiate between adenocarcinomas (AC) and squamous cell carcinomas (SCC) as the most common lung cancer by comparing their FDG uptake measured in the form of SUVmax. Methods A retrospective observational study, conducted at Ain Shams University Hospitals- Radiology Department patients diagnosed with non-small cell lung cancer (NSCLC) and histopathologically proved to be adenocarcinoma or squamous cell carcinoma. All were investigated using PET/CT scans in the period between June 2017 to September 2019. SUVmax of the lesion were measured and statistically analyzed compared with the histopathological type. Results A total of 30 patients were evaluated with the age ranging from 31 to 81 years old, the mean was 56.9 years (SD ± 11.53). Among them 25 patients (83.3%) were males and 5 patients (16.7%) were females. Out of the 30 NSCLC patients included in the study, 18 patients (60%) were histopathologically diagnosed as AC and 12 patients (40%) were histopathologically diagnosed as SCC. The mean SUVmax of the 30 NSCLC lesions was 15.02±8.4 (range: 2-32.8).The mean SUVmax of AC lesions was 12.4±7.55 (range: 2-32.8), while the mean SUVmax of SCC lesions was 18.95±8.3 (range: 3.7-30.77). The mean SUVmax of lesions in patients with SCC was significantly higher than that of patients with AC using Wilcoxon-Mann-Whitney test (P = 0.04). Conclusion SCC of the lung has higher SUVmax than AC of the lung and it can be used to differentiate between them using PET/CT.

Impact of optimized PET imaging conditions on 18F-FDG uptake quantification in patients with apparently normal aortas

BackgroundThe cardiovascular committee of the European Association of Nuclear Medicine (EANM) recently published recommendations on imaging conditions to be observed during 18F-FDG PET imaging of vascular inflammation. This study aimed to evaluate the impact of applying these optimized imaging conditions on PET quantification of arterial 18F-FDG uptake. Methods and ResultsFifty-seven patients were prospectively recruited to undergo an early 18F-FDG PET/CT imaging at 60 minutes and repeat delayed imaging at ≥ 120 minutes post tracer injection. Routine oncologic 18F-FDG PET protocol was observed for early imaging, while delayed imaging parameters were optimized for vascular inflammation imaging as recommended by the EANM. Aortic SUVmax of the ascending aorta and SUVmean from the lumen of the superior vena cava (SVC SUVmean) were obtained on early and delayed imaging. Target-to-background ratio (TBR) was obtained for the early and delayed imaging. Aortic SUVmax increased by a mean of 70%, while SVC SUVmean decreased by a mean of 52% between early and delayed imaging (P < 0.001). TBR increased by 122% following delayed imaging. TBR increased, while SVC SUVmean declined across all time-points from 120 to > 180 minutes. Aortic SUVmax significantly increased at imaging time-points between 120 and 180 minutes. No significant improvement in aortic SUVmax was seen at imaging time-points beyond 180 minutes. Conclusions18F-FDG PET imaging conditions optimized for vascular inflammation imaging lead to an improved quantification through an increase in the quantified vascular tracer uptake and decrease in blood-pool background activity.

Effect of Point Spread Function Deconvolution in Reconstruction of Brain 18F-FDG PET Images on the Diagnostic Thinking Efficacy in Alzheimer's Disease.

Purpose: This study aims to determine the effect of applying Point Spread Function (PSF) deconvolution, which is known to improve contrast and spatial resolution in brain 18F-FDG PET images, to the diagnostic thinking efficacy in Alzheimer's disease (AD).Methods: We compared Hoffman 3-D brain phantom images reconstructed with or without PSF. The effect of PSF deconvolution on AD diagnostic clinical performance was determined from digital brain 18F-FDG PET images of AD (n = 38) and healthy (n = 35) subjects compared to controls (n = 36). Performances were assessed with SPM at the group level (p < 0.001 for the voxel) and at the individual level by visual interpretation of SPM T-maps (p < 0.005 for the voxel) by the consensual analysis of three experienced raters.Results: A mix of large hypometabolic (1,483cm3, mean value of −867 ± 492 Bq/ml) and intense hypermetabolic (902 cm3, mean value of 1,623 ± 1,242 Bq/ml) areas was observed in the PSF compared to the no PSF phantom images. Significant hypometabolic areas were observed in the AD group compared to the controls, for reconstructions with and without PSF (respectively 23.7 and 26.2 cm3), whereas no significant hypometabolic areas were observed when comparing the group of healthy subjects to the control group. At the individual level, no significant differences in diagnostic performances for discriminating AD were observed visually (sensitivity of 89 and 92% for reconstructions with and without PSF respectively, similar specificity of 74%).Conclusion: Diagnostic thinking efficacy performances for diagnosing AD are similar for 18F-FDG PET images reconstructed with or without PSF.

Effect of Diet on Physiologic Bowel 18F-FDG Uptake.

Intestinal 18F-FDG uptake is variable in whole-body PET/CT. In cancer patients, particularly those suspected of relapse or metastasis, 18F-FDG absorption might interfere with scan interpretation. This study evaluated the effect of diet on intestinal 18F-FDG absorption. Methods: In total, 214 patients referring for oncologic 18F-FDG PET/CT participated. They were randomly divided into 2 groups and asked to follow either a routine diet (RD) or a low-carbohydrate, high-fat diet (LCHFD) for 24 h before the study. The small bowel and different parts of the colon (the cecum; the ascending, transverse, and descending segments; and the hepatic and splenic flexures) were evaluated and visually interpreted by nuclear medicine experts. Bowel uptake was graded through comparison with that in the liver as absent, mild, moderate, or severe. Results: Significantly higher 18F-FDG uptake in the descending colon (P = 0.001) and small intestine (P = 0.01) was observed in the RD group than in the LCHFD group. After patients with bowel cancer were omitted from the statistical analysis, no significant differences in the final results were seen. Conclusion: An LCHFD for 24 h before 18F-FDG PET imaging resulted in lower 18F-FDG uptake in the descending colon and small bowel than did an RD, assisting the interpreting physician by reducing the intestinal activity interference for more accurate diagnostic interpretation.

Metabolic Brain Network Analysis With 18F-FDG PET in a Rat Model of Neuropathic Pain.

Neuropathic pain has been found to be related to profound reorganization in the function and structure of the brain. We previously demonstrated changes in local brain activity and functional/metabolic connectivity among selected brain regions by using neuroimaging methods. The present study further investigated large-scale metabolic brain network changes in 32 Sprague–Dawley rats with right brachial plexus avulsion injury (BPAI). Graph theory was applied in the analysis of 2-deoxy-2-[18F] fluoro-D-glucose (18F-FDG) PET images. Inter-subject metabolic networks were constructed by calculating correlation coefficients. Global and nodal network properties were calculated and comparisons between pre- and post-BPAI (7 days) status were conducted. The global network properties (including global efficiency, local efficiency and small-world index) and nodal betweenness centrality did not significantly change for all selected sparsity thresholds following BPAI (p > 0.05). As for nodal network properties, both nodal degree and nodal efficiency measures significantly increased in the left caudate putamen, left medial prefrontal cortex, and right caudate putamen (p < 0.001). The right entorhinal cortex showed a different nodal degree (p < 0.05) but not nodal efficiency. These four regions were selected for seed-based metabolic connectivity analysis. Strengthened connectivity was found among these seeds and distributed brain regions including sensorimotor area, cognitive area, and limbic system, etc. (p < 0.05). Our results indicated that the brain had the resilience to compensate for BPAI-induced neuropathic pain. However, the importance of bilateral caudate putamen, left medial prefrontal cortex, and right entorhinal cortex in the network was strengthened, as well as most of their connections with distributed brain regions.

Can the BMI-based dose regimen be used to reduce injection activity and to obtain a constant image quality in oncological patients by 18F-FDG total-body PET/CT imaging?

PET image quality is influenced by the patient size according to the current guideline. The study aimed to propose an optimized dose regimen to yield a constant image quality independent of patient habitus to meet the clinical needs. A first patient cohort of 78 consecutive oncological patients (59.7 ± 13.7years) who underwent a total-body PET/CT scan were retrospectively enrolled to develop the regimen. The patients were randomly distributed in four body mass index (BMI) groups according to the World Health Organization (WHO) criteria. The liver SNR (signal-to-noise ratio, SNRL) was obtained by manually drawing regions of interest (ROIs) and normalized (SNRnorm) by the product of injected activity and acquisition time. Fits of SNRnorm against different patient-dependent parameters were performed to determine the best correlating parameter and fit method. A qualitative assessment on image quality was performed using a 5-point Likert scale to determine the acceptable threshold of SNRL. Thus, an optimized regimen was proposed and validated by a second patient cohort consisted of prospectively enrolled 38 oncological patients. The linear fit showed SNRnorm had the strongest correlation (R2 = 0.69) with the BMI than other patient-dependent parameters and fit method. The qualitative assessment indicated a SNRL value of 14.0 as an acceptable threshold to achieve sufficient image quality. The optimized dose regimen was determined as a quadratic relation with BMI: injected activity (MBq) = 39.2 (MBq)/(- 0.03*BMI + 1.49)2. In the validation study, the SNRL no longer decreased with the increase of BMI. There was no significant difference of the image quality regarding the value of SNRL between different BMI groups (p > 0.05). In addition, the injected activity was reduced by 75.6 ± 2.9%, 72.1 ± 4.0%, 67.1 ± 4.4%, and 64.8 ± 3.5% compared with the first cohort for the four BMI groups, respectively. The study proposed a quadratic relation between the 18F-FDG injected activity and the patient's BMI for total-body 18F-FDG PET imaging. In this regimen, the image quality can maintain in a constant level independent of patient habitus and meet the clinical requirement with a reduced injected activity.

Prognostic Significance of Regional/Systemic Metabolic Parameters on 18F-FDG PET in Pulmonary Lymphoepithelioma-Like Carcinoma.

BackgroundPulmonary lymphoepithelioma-like carcinoma (LELC) is a rare subtype of lung cancer with less than 700 cases being reported in the literature, and no specialized treatment guidelines have been established. The prognostic significance of metabolic parameters on 18F-FDG PET in pulmonary LELC still remains unknown.MethodsFrom July 2011 to September 2020, 76 pulmonary LELC patients undergoing pre-treatment 18F-FDG PET imaging were enrolled, and PET parameters including maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. In addition, whole-body tumor burdens were categorized into primary tumor lesion (PRL), thoracic lymph-node lesion (TRLN), and distant metastasis (DM) for respective metabolic parameters acquisition. ROC curves were generated to evaluate the predictive performance of the PET parameters, and correlations between tumor burdens of the different regional lesions were analyzed using linear correlation analysis. The prognostic significance for progression-free survival (PFS) and overall survival (OS) was assessed using univariate and multivariate survival analyses.ResultsTumor stage, pre-/post-treatment serum EBV-DNA copies, SUVmax (cutoff 17.5), MTV, and TLG were significantly associated with PFS and OS in univariate analysis. MTV and TLG (AUC = 0.862 and 0.857, respectively) showed significantly higher predictive value than SUVmax (AUC = 0.754) and remained independent prognostic indicators for PFS in multivariate analysis (P = 0.026 and 0.019, respectively). Besides, non-colinearity was detected between metabolic burdens of the different regional lesions. MTV-PRL, MTV-DM, TLG-PRL, and TLG-DM were identified to be independent prognostic factors for PFS and OS, whereas MTV-TRLN and TLG-TRLN were not.ConclusionThe study demonstrated that MTV and TLG had independent prognostic significance for pulmonary LELC, which supported the incorporation of 18F-FDG PET imaging into clinical treatment protocols for pulmonary LELC and implied multi-disciplinary cooperation for primary and distant metastatic lesions to further improve prognosis.

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using 18F-FDG PET Images.

Background: This study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) images. Methods: The local responses of 98 patients were categorized into two groups (complete response and noncomplete response). We performed a radiomics analysis using five segmentations created on FDG PET images, resulting in 4250 features per patient. To construct a machine learning model, we used the least absolute shrinkage and selection operator (LASSO) regression to extract radiomics features optimal for the prediction. Then, a prediction model was constructed by using a neural network classifier. The training model was evaluated with 5-fold cross-validation. Results: By the LASSO analysis of the training data, 22 radiomics features were extracted. In the testing data, the average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve score of the five prediction models were 89.6%, 92.7%, 89.5%, and 0.95, respectively. Conclusions: The proposed machine learning model using radiomics showed promising predictive accuracy of the local response of LA-ESCC treated by NCRT.

Dynamic 18F-FDG PET imaging of liver lesions: evaluation of a two-tissue compartment model with dual blood input function

BackgroundDynamic PET with kinetic modeling was reported to be potentially helpful in the assessment of hepatic malignancy. In this study, a kinetic modeling analysis was performed on hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) from dynamic FDG positron emission tomography/computer tomography (PET/CT) scans.MethodsA reversible two-tissue compartment model with dual blood input function, which takes into consideration the blood supply from both hepatic artery and portal vein, was used for accurate kinetic modeling of liver dynamic 18F-FDG PET imaging. The blood input functions were directly measured as the mean values over the VOIs on descending aorta and portal vein respectively. And the contribution of hepatic artery to the blood input function was optimization-derived in the process of model fitting. The kinetic model was evaluated using dynamic PET data acquired on 24 patients with identified hepatobiliary malignancy. 38 HCC or ICC identified lesions and 24 healthy liver regions were analyzed.ResultsResults showed significant differences in kinetic parameters {K}_{1}-{k}_{4}, blood supplying fraction {f}_{A}, and metabolic rate constant {K}_{i} between malignant lesions and healthy liver tissue. And significant differences were also observed in {K}_{1}, {k}_{3}, {f}_{A} and {K}_{i} between HCC and ICC lesions. Further investigations of the effect of SUV measurements on the derived kinetic parameters were conducted. And results showed comparable effectiveness of the kinetic modeling using either SUVmean or SUVmax measurements.ConclusionsDynamic 18F-FDG PET imaging with optimization-derived hepatic artery blood supply fraction dual-blood input function kinetic modeling can effectively distinguish malignant lesions from healthy liver tissue, as well as HCC and ICC lesions.

Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review.

Artificial intelligence (AI) is a growing field of research that is emerging as a promising adjunct to assist physicians in detection and management of patients with cancer. 18F-FDG PET imaging helps physicians in detection and management of patients with cancer. In this study we discuss the possible applications of AI in 18F-FDG PET imaging based on the published studies. A systematic literature review was performed in PubMed on early August 2020 to find the relevant studies. A total of 65 studies were available for review against the inclusion criteria which included studies that developed an AI model based on 18F-FDG PET data in cancer to diagnose, differentiate, delineate, stage, assess response to therapy, determine prognosis, or improve image quality. Thirty-two studies met the inclusion criteria and are discussed in this review. The majority of studies are related to lung cancer. Other studied cancers included breast cancer, cervical cancer, head and neck cancer, lymphoma, pancreatic cancer, and sarcoma. All studies were based on human patients except for one which was performed on rats. According to the included studies, machine learning (ML) models can help in detection, differentiation from benign lesions, segmentation, staging, response assessment, and prognosis determination. Despite the potential benefits of AI in cancer imaging and management, the routine implementation of AI-based models and 18F-FDG PET-derived radiomics in clinical practice is limited at least partially due to lack of standardized, reproducible, generalizable, and precise techniques.

Early SUVmax is the best predictor of axillary lymph node metastasis in stage III breast cancers.

Although fluorine-18-labeled 2-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission/computed tomography (PET/CT) imaging has been investigated for its ability to evaluate lymph node metastasis of breast cancer, few comparative analyses have evaluated the preoperative and postoperative regional lymph node metastasis of breast cancer by dual-phase imaging, especially in patients with stage III (N2) disease. The clinical, pathological, and imaging data of 40 patients with stage III (N2) breast cancer were retrospectively analyzed. All patients underwent dual-time point 18F-FDG imaging before surgery and postoperative pathology examinations were obtained. The short-axis lymph node diameter was measured, and the maximum standardized uptake value (SUVmax) and the percentage difference of SUVmax between dual-phase (ΔSUVmax) were calculated to compare metastatic and non-metastatic lymph nodes on dual-time point images. A total of 398 axillary lymph nodes were inspected, and 209 lymph nodes were matched with those on PET/CT images, including 97 metastatic and 112 non-metastatic lymph nodes. The SUVmax values were significantly different between metastatic and non-metastatic lymph nodes, in both the early and delayed scans (P<0.001). For metastatic lymph nodes, the SUVmax value on the delayed scan (6.17±2.62) was significantly higher compared with the early scan (5.45±1.35; ΔSUVmax =0.08±0.21, P<0.001). Moreover, the SUVmax values were not significantly different between the delayed (2.82±0.91) and early scans (2.79±0.72; ΔSUVmax=-0.00±0.11, P=0.77). The short diameters were not significantly different between metastatic and non-metastatic lymph nodes (P=0.12), and the SUVmax values of metastatic lymph nodes with short diameters of >4.00 and ≤6.00 mm were not significantly different between the early and delayed scans (P=0.06). However, the SUVmax values of metastatic lymph nodes with short diameters of >6.00 and ≤8.00 mm (7.11±0.19 vs. 5.96±0.08) and short diameters of >8.00 and ≤10.00 mm (10.76±0.35 vs. 6.82±0.50) were higher on the delayed scan versus the early scan, respectively (P<0.01 for each comparison). The difference between the ΔSUVmax values among the three subgroups was statistically significant (F=78.98, P<0.001).The receiver operating characteristic (ROC) curve analysis of the lymph nodes showed that the area under the curve (AUC) of the early and delayed PET/CT scans was 0.961 (0.925-0.983, P=0.013) and 0.897 (0.847-0.934, P=0.022), respectively. The ROC curves of the early and delayed scans were also significantly different (z=4.46, P<0.001). AUC of the ΔSUVmax for the early scan was significantly lower compared with delayed scans (z=8.95 vs. 9.13, respectively; P<0.001). Dual-time point 18F-FDG PET imaging significantly improved the prediction and detection of axillary lymph node metastasis, compared with prediction based on size of lymph node alone, in patients with stage III breast cancer. We found that lymph nodes with continuously increased SUVmax values tended to show metastasis, and early SUVmax assessment offers the best capacity for prediction of axillary lymph node metastasis.

Radiation-Induced Metabolic Shifts in the Hepatic Parenchyma: Findings from 18F-FDG PET Imaging and Tissue NMR Metabolomics in a Mouse Model for Hepatocellular Carcinoma

Purpose: By taking advantage of 18F-FDG PET imaging and tissue nuclear magnetic resonance (NMR) metabolomics, we examined the dynamic metabolic alterations induced by liver irradiation in a mouse model for hepatocellular carcinoma (HCC). Methods: After orthotopic implantation with the mouse liver cancer BNL cells in the right hepatic lobe, animals were divided into two experimental groups. The first received irradiation (RT) at 15 Gy, while the second (no-RT) did not. Intergroup comparisons over time were performed, in terms of 18F-FDG PET findings, NMR metabolomics results, and the expression of genes involved in inflammation and glucose metabolism. Results: As of day one post-irradiation, mice in the RT group showed an increased 18F-FDG uptake in the right liver parenchyma compared with the no-RT group. However, the difference reached statistical significance only on the third post-irradiation day. NMR metabolomics revealed that glucose concentrations peaked on day one post-irradiation both, in the right and left lobes—the latter reflecting a bystander effect. Increased pyruvate and glutamate levels were also evident in the right liver on the third post-irradiation day. The expression levels of the glucose-6-phosphatase (G6PC) and fructose-1, 6-bisphosphatase 1 (FBP1) genes were down-regulated on the first and third post-irradiation days, respectively. Therefore, liver irradiation was associated with a metabolic shift from an impaired gluconeogenesis to an enhanced glycolysis from the first to the third post-irradiation day. Conclusion: Radiation-induced metabolic alterations in the liver parenchyma occur as early as the first post-irradiation day and show dynamic changes over time.

Metabolic Scar Assessment with18F-FDG PET: Correlation to Ischemic Ventricular Tachycardia Substrate and Successful Ablation Sites.

The functional and molecular imaging characteristics of ischemic ventricular tachycardia (VT) substrate are incompletely understood. Our objective was to compare regional 18F-FDG PET tracer uptake with detailed electroanatomic maps (EAMs) in a more extensive series of postinfarction VT patients to define the metabolic properties of VT substrate and successful ablation sites. Methods: Three-dimensional (3D) metabolic left ventricular reconstructions were created from perfusion-normalized 18F-FDG PET images in consecutive patients undergoing VT ablation. PET defects were classified as severe (defined as <50% uptake) or moderate (defined as 50%-70% uptake), as referenced to the maximal 17-segment uptake. Color-coded PET scar reconstructions were coregistered with corresponding high-resolution 3D EAMs, which were classified as indicating dense scarring (defined as voltage < 0.5 mV), normal myocardium (defined as voltage > 1.5 mV), or border zones (defined as voltage of 0.5-1.5 mV). Results: All 56 patients had ischemic cardiomyopathy (ejection fraction, 29% ± 12%). Severe PET defects were larger than dense scarring, at 63.0 ± 48.4 cm2 versus 13.8 ± 33.1 cm2 (P < 0.001). Similarly, moderate/severe PET defects (≤70%) were larger than areas with abnormal voltage (≤1.5 mV) measuring 105.1 ± 67.2 cm2 versus 56.2 ± 62.6 cm2 (P < 0.001). Analysis of bipolar voltage (23,389 mapping points) showed decreased voltage among severe PET defects (n = 10,364; 0.5 ± 0.3 mV) and moderate PET defects (n = 5,243; 1.5 ± 0.9 mV, P < 0.01), with normal voltage among normal PET areas (>70% uptake) (n = 7,782, 3.2 ± 1.3 mV, P < 0.001). Eighty-eight percent of VT channel or exit sites (n = 44) were metabolically abnormal (severe PET defect, 78%; moderate PET defect, 10%), whereas 12% (n = 6) were in PET-normal areas. Metabolic channels (n = 26) existed in 45% (n = 25) of patients, with an average length and width of 17.6 ± 12.5 mm and 10.3 ± 4.2 mm, respectively. Metabolic channels were oriented predominantly in the apex or base (86%), harboring VT channel or exit sites in 31%. Metabolic rapid-transition areas (>50% change in 18F-FDG tracer uptake/15 mm) were detected in 59% of cases (n = 33), colocalizing to VT channels or exit sites (15%) or near these sites (85%, 12.8 ± 8.5 mm). Metabolism-voltage mismatches in which there was a severe PET defect but voltage indicating normal myocardium were seen in 21% of patients (n = 12), 41% of whom were harboring VT channel or exit sites. Conclusion: Abnormal 18F-FDG uptake categories could be detected using incremental 3D step-up reconstructions. They predicted decreasing bipolar voltages and VT channel or exit sites in about 90% of cases. Additionally, functional imaging allowed detection of novel molecular tissue characteristics within the ischemic VT substrate such as metabolic channels, rapid-transition areas, and metabolism-voltage mismatches demonstrating intrasubstrate heterogeneity and providing possible targets for imaging-guided ablation.

Rosai-Dorfman-Destombes (RDD) disease presenting as palindromic rheumatism

BackgroundRosai-Dorfman-Destombes (RDD) disease, is a rare proliferative and inflammatory disorder of non-Langerhans cell histiocytes.Case presentationWe report a 35-year-old woman, who originally presented with recurrent episodes of lower extremity joint/bone pain and chronic nasal stuffiness and congestion. Her worsening nasal congestion was due to an obstructing nasal cavity lesion which was subsequently biopsied. Pathology was consistent with RDD. 18F-FDG PET images demonstrated intense uptake in the paranasal sinuses and a large pelvic lymph node. Focal osseous lesions with intense 18F-FDG uptake were also observed in the lower extremity, corresponding to areas of peri-articular pain. Rheumatologic work-up was consistent with palindromic rheumatism. She was diagnosed with immune-related disseminated RDD, presenting as palindromic rheumatism.ConclusionsThis is the first case of RDD presenting as palindromic rheumatism. RDD should be considered as a possible but rare diagnosis in young patients with sinus-related symptoms and lymphadenopathy. The disease can on rare occasions be disseminated and can also present as immune-related RDD, such as in this patient.

Clinical Utility of 18F-FDG PET in Neuroendocrine Tumors Prior to Peptide Receptor Radionuclide Therapy: A Systematic Review and Meta-Analysis

Simple SummaryFunctional imaging with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has evolved into a major clinical tool in cancer diagnosis and management for many malignancies in diverse clinical settings, providing valuable information on tumor behavior and aggressiveness. In the field of neuroendocrine tumors (NETs), recent advances in molecular imaging and targeted treatments with novel theranostic agents favor a more patient-tailored approach. Although peptide receptor radionuclide therapy (PRRT) has recently become an established therapy for progressive NETs, the role of 18F-FDG PET prior to PRRT in patients with NETs of different origins and grades remains to be determined. Herein, we provide a comprehensive summary of available evidence in contemporary literature by means of a systematic review and meta-analysis, demonstrating that dual-functional imaging with 68Ga-DOTA-peptides and 18F-FDG prior to PRRT appears to be a useful tool in NET management by delineating tumor somatostatin receptor expression and glycolytic metabolic activity, and predicting tumor response and survival outcomes.The role of 18F-FDG PET in patients with variable grades of neuroendocrine tumors (NETs) prior to peptide receptor radionuclide therapy (PRRT) has not been adequately elucidated. We aimed to evaluate the impact of 18F-FDG PET status on disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) in neuroendocrine tumor (NET) patients receiving PRRT. We searched the MEDLINE, Embase, Cochrane Library, and Web of Science databases up to July 2020 and used the Newcastle-Ottawa scale (NOS) criteria to assess quality/risk of bias. A total of 5091 articles were screened. In 12 studies, 1492 unique patients with NETs of different origins were included. The DCR for patients with negative 18F-FDG PET status prior to PRRT initiation was 91.9%, compared to 74.2% in patients with positive 18F-FDG PET status (random effects odds ratio (OR): 4.85; 95% CI: 2.27–10.36). Adjusted analysis of pooled hazard ratios (HRs) confirmed longer PFS and OS in NET patients receiving PRRT with negative 18F-FDG PET (random effects HR:2.45; 95%CIs: 1.48–4.04 and HR:2.25; 95% CIs:1.55–3.28, respectively). In conclusion, 18F-FDG PET imaging prior to PRRT administration appears to be a useful tool in NET patients to predict tumor response and survival outcomes and a negative FDG uptake of the tumor is associated with prolonged PFS and OS.

BIMG-11. PHARMACODYNAMIC EVALUATION OF IDH AND EGFR INHIBITION IN HUMAN GLIOMAS USING MOLECULAR MRI

Metabolic differences are inherent to specific glioma subtypes and can be altered using targeted treatments, including IDH and EGFR inhibition. Using a large cohort of patients scanned at UCLA and other centers over the last 5 years, we demonstrate that IDH, 1p19q, and EGFR alterations uniquely contribute to alterations in glycolysis and oxygen utilization using a clinically available molecular MRI technique termed amine chemical exchange saturation transfer spin-and-gradient-echo echoplanar imaging (CEST-SAGE-EPI). Our data shows that CEST-SAGE-EPI estimates of tumor acidity are strongly associated with the degree of glycolysis as evaluated with direct pH measurements, quantitative IHC, bioenergetics experiments, and correlations with 18F-FDG PET images. Data further reveals that IDH wild type gliomas have higher acidity and oxygen utilization compared with IDH mutant gliomas, 1p19q non-codeleted gliomas (astrocytomas) have higher tumor acidity compared to 1p19q codeleted gliomas (oligodendrogliomas), and EGFR amplified gliomas have higher oxygen utilization compared with non-amplified gliomas. Additionally, phase II clinical trial data suggests successful IDH inhibition results in an early and measurable increase in tumor acidity and further reduction in oxygen utilization, signifying suppression of oxidative phosphorylation and/or glutaminolysis in favor of glycolysis. Alternatively, phase II clinical trial data suggests successful EGFR inhibition with brain penetrant agents results in early reductions in tumor acidity and 18F-FDG PET uptake, consistent with a reduction in glycolysis. Data also indicates that continual increases in tumor acidity during routine follow-up after initial therapeutic changes results in uniformly worse outcomes in all tumor subtypes under all mentioned treatment scenarios.

Correlation Between Dual-Time-Point FDG PET and Tumor Microenvironment Immune Types in Non-Small Cell Lung Cancer.

PurposeDual-time-point 18F-fluorodeoxyglucose positron emission tomography (DTP 18F-FDG PET), which reflects the dynamics of tumor glucose metabolism, may also provide a novel approach to the characterization of both cancer cells and immune cells within the tumor immune microenvironment (TIME). We investigated the correlations between the metabolic parameters (MPs) of DTP 18F-FDG PET images and the tumor microenvironment immune types (TMITs) in patients with non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was performed in 91 patients with NSCLC who underwent preoperative DTP 18F-FDG PET/CT scans. MPs in the early scan (eSUVmax, eSUVmean, eMTV, eTLG) and delayed scan (dSUVmax, dSUVmean, dMTV, dTLG) were calculated, respectively. The change in MPs (ΔSUVmax, ΔSUVmean, ΔMTV, ΔTLG) between the two time points were calculated. Tumor specimens were analyzed by immunohistochemistry for PD-1/PD-L1 expression and CD8+ tumor-infiltrating lymphocytes (TILs). TIME was classified into four immune types (TMIT I ~ IV) according to the expression of PD-L1 and CD8+ TILs. Correlations between MPs with TMITs and the immune-related biomarkers were analyzed. A composite metabolic signature (Meta-Sig) and a combined model of Meta-Sig and clinical factors were constructed to predict patients with TMIT I tumors.ResultseSUVmax, eSUVmean, dSUVmax, dSUVmean, ΔSUVmax, ΔSUVmean, and ΔTLG were significantly higher in PD-L1 positive patients (p = 0.0007, 0.0006, < 0.0001, < 0.0001, 0.0002, 0.0002, 0.0247, respectively), and in TMIT-I tumors (p = 0.0001, < 0.0001, < 0.0001, < 0.0001, 0.0009, 0.0009, 0.0144, respectively). Compared to stand-alone MP, the Meta-Sig and combined model displayed better performance for assessing TMIT-I tumors (Meta-sig: AUC = 0.818, sensitivity = 86.36%, specificity = 73.91%; Model: AUC = 0.869, sensitivity = 77.27%, specificity = 82.61%).ConclusionHigh glucose metabolism on DTP 18F-FDG PET correlated with the TMIT-I tumors, and the Meta-Sig and combined model based on clinical and metabolic information could improve the performance of identifying the patients who may respond to immunotherapy.

The role of 18F-Fluorodeoxyglucose PET/CT in restaging patients with small cell lung cancer: a systematic review.

18F-Fluorodeoxyglucose (FDG) PET imaging may play an important role in the restaging of patients with small-cell lung cancer (SCLC),, nevertheless, a systematic review of literature was still missing in this setting. The aim of this review was to summarize the evidence on literature regarding the utility of 18F-FDG PET imaging in restaging patients with SCLC. A literature search was performed to retrieve original studies using 18F-FDG PET or 18F-FDG PET/computed tomography (CT) in a minimum of 10 patients with SCLC at restaging. The selected literature (17 studies) was discussed in four sections: detection rate, impact on management, prediction of prognosis and evaluation of the response to therapy. According to the literature, PET imaging may result in discordance with conventional imaging, mainly contrast-enhanced CT (ceCT), and detect additional lesions in a certain proportion of cases, leading to upstaging or downstaging. A variable level of disagreement between PET and conventional imaging has been reported also in the evaluation of response to therapy. A positive PET study is associated with shorter survival, especially in the presence of distant metastases. According to some studies, semiquantitative parameters are also inversely associated with overall survival and progression-free survival. Although the retrieved articles proved the utility of 18F-FDG PET imaging in each clinical setting, literature is still limited. This review encourages the use of 18F-FDG PET imaging, especially in conjunction with ceCT in recurrent SCLC patients. Further level I evidence is needed to further assess the diagnostic and prognostic capability of 18F-FDG PET/ceCT findings in SCLC.

Noninvasive quantification of nonhuman primate dynamic 18F-FDG PET imaging

18F-FDG uptake rate constant Ki is the main physiology parameter measured in dynamic PET studies. A model-independent graphical analysis using Patlak plot with plasma input function (PIF) is a standard approach used to estimate Ki . The PIF is the 18F-FDG time activity curve (TAC) in plasma that is obtained by serial arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach with reduced blood samples for noninvasive quantification of dynamic 18F-FDG PET imaging. Eight 60 min rhesus monkey brain dynamic 18F-FDG PET scans with arterial blood samples were collected. The measured PIF (mPIF) was determined by arterial blood samples. TACs of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the population-based PIF (pPIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal sampling scheme with given blood sample size was determined by maximizing the correlations between the Ki estimated from pPIF and those obtained by mPIF. A leave-two-out cross-validation method was used for evaluation. The linear correlations between the Ki estimates from pPIF with optimal sampling schemes and those from mPIF were: Ki (pPIF 1 sample at 40 min) = 1.015 Ki (mPIF) − 0.000, R 2 = 0.974; Ki (pPIF 2 samples at 35 and 50 min) = 1.052 Ki (mPIF) − 0.001, R 2 = 0.976; Ki (pPIF 3 samples at 12, 40, and 50 min) = 1.030 Ki (mPIF) − 0.000, R 2 = 0.985; and Ki (pPIF 4 samples at 10, 20, 40, and 50 min) = 1.016 Ki (mPIF)- 0.000, R 2 = 0.993. As the sample size became greater or equal to 4, the Ki estimates from pPIF with the optimal protocol were almost identical to those from mPIF. The Patlak plot-based optimization approach is a reliable method to estimate PIF for noninvasive quantification of non-human primate dynamic 18F-FDG PET imaging and is potentially extendable to further translational human studies.

Brain 18F-FDG PET for the diagnosis of autoimmune encephalitis: a systematic review and a meta-analysis

To consolidate current understanding of detection sensitivity of brain 18F-FDG PET scans in the diagnosis of autoimmune encephalitis and to define specific metabolic imaging patterns for the most frequently occurring autoantibodies. A systematic and exhaustive search of data available in the literature was performed by querying the PubMed/MEDLINE and Cochrane databases for the search terms: ((PET) OR (positron emission tomography)) AND ((FDG) OR (fluorodeoxyglucose)) AND ((encephalitis) OR (brain inflammation)). Studies had to satisfy the following criteria: (i) include at least ten pediatric or adult patients suspected or diagnosed with autoimmune encephalitis according to the current recommendations, (ii) specifically present 18F-FDG PET and/or morphologic imaging findings. The diagnostic 18F-FDG PET detection sensitivity in autoimmune encephalitis was determined for all cases reported in this systematic review, according to a meta-analysis following the PRISMA method, and selected publication quality was assessed with the QUADAS-2 tool. The search strategy identified 626 articles including references from publications. The detection sensitivity of 18F-FDG PET was 87% (80-92%) based on 21 publications and 444 patients included in the meta-analysis. We also report specific brain 18F-FDG PET imaging patterns for the main encephalitis autoantibody subtypes. Brain 18F-FDG PET has a high detection sensitivity and should be included in future diagnostic autoimmune encephalitis recommendations. Specific metabolic 18F-FDG PET patterns corresponding to the main autoimmune encephalitis autoantibody subtypes further enhance the value of this diagnostic.