18F-FDG Positron Emission Tomography Imaging Research Articles

Inflammatory and immunopathological differences in brains of permissive and non-permissive hosts with Angiostrongylus cantonensis infection can be identified using 18F/FDG/PET-imaging.

Angiostrongylus cantonensis is a parasite that mainly infects the heart and pulmonary arteries of rats and causes human eosinophilic meningitis or meningoencephalitis in certain geographical areas. Current diagnostic methods include detection of the parasite in cerebrospinal fluid (CSF) and eosinophilic immune examination after lumbar puncture, which may be risky and produce false-positive results. 18F- Fluorodeoxyglucose (FDG), a Positron emission tomography (PET) tracer, has been used to assess different pathological or inflammatory changes in the brains of patients. In this study, we hypothesized that A. cantonensis infection-induced inflammatory and immunomodulatory factors of eosinophils result in localized pathological changes in the brains of non-permissive hosts, which could be analyzed using in vivo 18F-FDG PET imaging. Non-permissive host ICR mice and permissive host SD rats were infected with A. cantonensis, and the effects of the resulting inflammation on 18F-FDG uptake were characterized using PET imaging. We also quantitatively measured the distributed uptake values of different brain regions to build an evaluated imaging model of localized neuropathological damage caused by eosinophilic inflammation. Our results showed that the uptake of 18F-FDG increased in the cerebellum, brainstem, and limbic system of mice at three weeks post-infection, whereas the uptake in the rat brain was not significant. Immunohistochemical staining and western blotting revealed that Iba-1, a microglia-specific marker, significantly increased in the hippocampus and its surrounding area in mice after three weeks of infection, and then became pronounced after four weeks of infection; while YM-1, an eosinophilic chemotactic factor, in the hippocampus and midbrain, increased significantly from two weeks post-infection, sharply escalated after three weeks of infection, and peaked after four weeks of infection. Cytometric bead array (CBA) analysis revealed that the expression of TNF in the serum of mice increased concomitantly with the prolongation of infection duration. Furthermore, IFN-γ and IL-4 in rat serum were significantly higher than in mouse serum at two weeks post-infection, indicating significantly different immune responses in the brains of rats and mice. We suggest that 18F-FDG uptake in the host brain may be attributed to the accumulation of large numbers of immune cells, especially the metabolic burst of activated eosinophils, which are attracted to and induced by activated microglia in the brain. An in vivo 18F-FDG/PET imaging model can be used to evaluate live neuroinflammatory pathological changes in the brains of A. cantonensis-infected mice and rats.

Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions

PurposeThis study aimed to evaluate the clinical feasibility of early 30-minute dynamic 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) scanning protocol for patients with lung lesions in comparison to the standard 65-minute dynamic FDG-PET scanning as a reference.MethodsDynamic 18F-FDG PET images of 146 patients with 181 lung lesions (including 146 lesions confirmed by histology) were analyzed in this prospective study. Dynamic images were reconstructed into 28 frames with a specific temporal division protocol for the scan data acquired 65 min post-injection. Ki images and quantitative parameters Ki based on two different acquisition durations [the first 30 min (Ki-30 min) and 65 min (Ki-65 min)] were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. The two acquisition durations were compared for Ki image quality (including visual score analysis and number of lesions detected) and Ki value (including accuracy of Ki, the value of differential diagnosis of lung lesions and prediction of PD-L1 status) by Wilcoxon’s rank sum test, Spearman’s rank correlation analysis, receiver operating characteristic (ROC) curve, and the DeLong test. The significant testing level (alpha) was set to 0.05.ResultsThe quality of the Ki-30 min images was not significantly different from the Ki-65 min images based on visual score analysis (P > 0.05). In terms of Ki value, among 181 lesions, Ki-65 min was statistically higher than Ki-30 min (0.027 ± 0.017 ml/g/min vs. 0.026 ± 0.018 ml/g/min, P < 0.05), while a very high correlation was obtained between Ki-65 min and Ki-30 min (r = 0.977, P < 0.05). In the differential diagnosis of lung lesions, ROC analysis was performed on 146 histologically confirmed lesions, the area under the curve (AUC) of Ki-65 min, Ki-30 min, and SUVmax was 0.816, 0.816, and 0.709, respectively. According to the Delong test, no significant differences in the diagnostic accuracies were found between Ki-65 min and Ki-30 min (P > 0.05), while the diagnostic accuracies of Ki-65 min and Ki-30 min were both significantly higher than that of SUVmax (P < 0.05). In 73 (NSCLC) lesions with definite PD-L1 expression results, the Ki-65 min, Ki-30 min, and SUVmax in PD-L1 positivity were significantly higher than that in PD-L1 negativity (P < 0.05). And no significant differences in predicting PD-L1 positivity were found among Ki-65 min, Ki-30 min, and SUVmax (AUC = 0.704, 0.695, and 0.737, respectively, P > 0.05), according to the results of ROC analysis and Delong test.ConclusionsThis study indicates that an early 30-minute dynamic FDG-PET acquisition appears to be sufficient to provide quantitative images with good-quality and accurate Ki values for the assessment of lung lesions and prediction of PD-L1 expression. Protocols with a shortened early 30-minute acquisition time may be considered for patients who have difficulty with prolonged acquisitions to improve the efficiency of clinical acquisitions.

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer

BackgroundTo investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC).MethodsWe retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model.ResultsThe model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585–0.909) on the training cohort and that of 0.701 (95% CI: 0.468–0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection.ConclusionThe habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.

Strategies for deep learning-based attenuation and scatter correction of brain 18 F-FDG PET images in the image domain.

Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging. In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space. Three different deep learning methods were developed for direct AC in the image space: (i) use of non-attenuation-corrected PET images as input (NonAC-PET), (ii) use of attenuation-corrected PET images with a simple two-class AC map (composed of soft-tissue and background air) obtained from NonAC-PET images (PET segmentation-based AC [SegAC-PET]), and (iii) use of both NonAC-PET and SegAC-PET images in a Double-Channel fashion to predict ground truth attenuation corrected PET images with Computed Tomography images (CTAC-PET). Since a simple two-class AC map (generated from NonAC-PET images) can easily be generated, this work assessed the added value of incorporating SegAC-PET images into direct AC in the image space. A 4-fold cross-validation scheme was adopted to train and evaluate the different models based using 80 brain 18 F-Fluorodeoxyglucose PET/CT images. The voxel-wise and region-wise accuracy of the models were examined via measuring the standardized uptake value (SUV) quantification bias in different regions of the brain. The overall root mean square error (RMSE) for the Double-Channel setting was 0.157±0.08 SUV in the whole brain region, while RMSEs of 0.214±0.07 and 0.189±0.14 SUV were observed in NonAC-PET and SegAC-PET models, respectively. A mean SUV bias of 0.01±0.26% was achieved by the Double-Channel model regarding the activity concentration in cerebellum region, as opposed to 0.08±0.28% and 0.05±0.28% SUV biases for the network that uniquely used NonAC-PET or SegAC-PET as input, respectively. SegAC-PET images with an SUV bias of -1.15±0.54%, served as a benchmark for clinically accepted errors. In general, the Double-Channel network, relying on both SegAC-PET and NonAC-PET images, outperformed the other AC models. Since the generation of two-class AC maps from non-AC PET images is straightforward, the current study investigated the potential added value of incorporating SegAC-PET images into a deep learning-based direct AC approach. Altogether, compared with models that use only NonAC-PET and SegAC-PET images, the Double-Channel deep learning network exhibited superior attenuation correction accuracy.

PET4 Response As an Independent Predictor of Long-Term Outcomes in ECHELON2 (A+CHP Vs. CHOP) in CD30+PTCL

Background: Peripheral T-cell lymphomas (PTCLs) are uncommon, heterogeneous, and often aggressive subtypes of lymphoma characterized by a high risk of progression even after combination chemotherapy. The phase III, ECHELON-2 trial showed that the combination of brentuximab vedotin with chemotherapy (A+CHP) in patients (pts) with CD30-positive PTCLs improved progression-free survival (PFS) and overall survival (OS) compared to CHOP. In PTCL, positron emission tomography (PET) is a valuable way to assess disease burden and stage at the time of diagnosis and evaluate treatment response. Prior retrospective studies have shown that interim and end of treatment PET may predict outcomes; however, there are no prospective studies confirming their predictive value. In the ECHELON-2 study, PET4 outcomes were prospectively collected, and the objective of this exploratory analysis is to investigate the potential for interim PET scans to predict treatment response and long-term outcomes. Objective: To evaluate the role of interim 18F-FDG PET imaging (PET4) in predicting end of treatment (EOT) response, progression free survival (PFS), and overall survival (OS) in pts (pts) with CD30+PTCL treated with A+CHP or CHOP in the ECHELON-2 trial. Methods: The ECHELON-2 trial included prospective 18F-FDG PET scans and assessment of treatment response including long-term PFS per investigator and OS. PET4 status was based on Deauville score by Independent Review Facility (IRF) assessment using scans at the cycle 4 response assessment. Deauville scores 1-3 are considered negative (PET4neg) and 4-5 positive (PET4pos). EOT response is the best response after completion of study treatment and prior to long term follow up per Cheson 2007 by IRF assessment. Kaplan-Meier methods were used to estimate PFS and OS; p-values are based on stratified log-rank tests. All analyses are exploratory, and p-values are descriptive. Results: In this study, 452 pts were included, with 226 randomized to the A+CHP treatment arm and 226 to the CHOP treatment arm; median follow up was 66.8 months (range 0-90). Of randomized pts, 32 pts in the A+CHP arm and 41 pts in the CHOP arm were not evaluable for PET4. The overall results showed that the PET4neg evaluable subgroup had a higher proportion of pts achieving complete responses (CR) than PET4pos (80% vs. 9%) at EOT (Table 1). In pts assessed for PET4, pts who were PET4neg had improved PFS and OS in both the A+CHP and CHOP treatment arms (Figure 1). For A+CHP pts, those who achieved PET4neg had an improved PFS [HR 0.36, CI-95 (0.19-0.66), p=0.0006] and OS [HR 0.38, CI-95 (0.18-0.78), p=0.0060] compared to those who were PET4pos. Similarly, pts in the CHOP arm who achieved PET4neg had an improved PFS [HR 0.26, CI-95 (0.17-0.41), p&amp;lt;0.0001] and OS [HR 0.24, CI-95 (0.14-0.41), p&amp;lt; 0.0001] compared to those who were PET4pos. Among pts with anaplastic large cell lymphoma (sALCL) (n=316), 19 pts in the A+CHP arm and 32 in the CHOP arm were not evaluable for PET4. The results in this subgroup show that 128/143 (90%) A+CHP pts who had PET4 assessments achieved PET4neg. These pts experienced improved PFS [HR 0.28, CI-95 (0.14-0.60), p=0.0004] and OS [HR 0.38, CI-95 (0.16-0.94), p 0.0292] compared to PET4pos pts. For CHOP pts who had PET4 assessments 98/122 (80%), achieved PET4neg; these pts experienced improved PFS [HR 0.31, CI-95 (0.12-0.55), p&amp;lt;0.0001] and improved OS [HR 0.25, CI-95 (0.12-0.25), p= 0.0002] compared to PET4pos pts. Conclusion: In an exploratory analysis of interim PET imaging as part of the ECHELON-2 study, PET4neg by Deauville was associated with improved PFS and OS in both the A+CHP and CHOP arms. Our findings support the use of PET4 response in PTCL as a predictor of outcomes in both the A+CHP and CHOP pts. These findings emphasize the potential of PET scans to enhance risk stratification, individualize therapy decisions, and improve pt outcomes in the management of PTCLs. Further analysis will be presented at the meeting.

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm.

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality,decreased administered activity and/or acquisition times. To evaluate the impact of reducing counts(simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images. Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60s/bed, β=800; 50s/bed, β=900; 40s/bed, β=1000; and 30s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions. The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90s/bed, β=700 only at a simulated acquisition time of 30s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40s/bed, β=1000 images were generally preferred over 60s/bed TOF OSEM images. In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

EAMNet: an Alzheimer’s disease prediction model based on representation learning

Objective. Brain 18F-FDG PET images indicate brain lesions’ metabolic status and offer the predictive potential for Alzheimer’s disease (AD). However, the complexity of extracting relevant lesion features and dealing with extraneous information in PET images poses challenges for accurate prediction. Approach. To address these issues, we propose an innovative solution called the efficient adaptive multiscale network (EAMNet) for predicting potential patient populations using positron emission tomography (PET) image slices, enabling effective intervention and treatment. Firstly, we introduce an efficient convolutional strategy to enhance the receptive field of PET images during the feature learning process, avoiding excessive extraction of fine tissue features by deep-level networks while reducing the model’s computational complexity. Secondly, we construct a channel attention module that enables the prediction model to adaptively allocate weights between different channels, compensating for the spatial noise in PET images’ impact on classification. Finally, we use skip connections to merge features from different-scale lesion information. Through visual analysis, the network constructed in this article aligns with the regions of interest of clinical doctors. Main results. Through visualization analysis, our network aligns with regions of interest identified by clinical doctors. Experimental evaluations conducted on the ADNI (Alzheimer’s Disease Neuroimaging Initiative) dataset demonstrate the outstanding classification performance of our proposed method. The accuracy rates for AD versus NC (Normal Controls), AD versus MCI (Mild Cognitive Impairment), MCI versus NC, and AD versus MCI versus NC classifications achieve 97.66%, 96.32%, 95.23%, and 95.68%, respectively. Significance. The proposed method surpasses advanced algorithms in the field, providing a hopeful advancement in accurately predicting and classifying Alzheimer’s Disease using 18F-FDG PET images. The source code has been uploaded to https://github.com/Haoliang-D-AHU/EAMNet/tree/master.

Prediction of synchronous distant metastasis of primary pancreatic ductal adenocarcinoma using the radiomics features derived from 18F-FDG PET and MRI

To explore the potential of the joint radiomics analysis of positron-emission tomography (PET) and magnetic resonance imaging (MRI) of primary tumours for predicting the risk of synchronous distant metastasis (SDM) in patients with pancreatic ductal adenocarcinoma (PDAC). 18F-FDG PET and MRI images of PDAC patients from January 2011 to December 2020 were collected retrospectively. Patients (n=66) who received 18F-FDG PET/CT and MRI were included in a development group. Patients (n=25) scanned with hybrid PET/MRI were incorporated in an external test group. A radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm to select PET-MRI radiomics features of primary PDAC tumours. A radiomics nomogram was developed by combining the radiomics signature and important clinical indicators using univariate and multivariate analysis to assess patients' metastasis risk. The nomogram was verified with the employment of an external test group. Regarding the development cohort, the radiomics nomogram was found to be better for predicting the risk of distant metastasis (area under the curve [AUC]: 0.93, sensitivity: 87%, specificity: 85%) than the clinical model (AUC: 0.70, p<0.001; sensitivity:70%, specificity: 65%) and the radiomics signature (AUC: 0.89, p>0.05; sensitivity: 65%, specificity:100%). Concerning the external test cohort, the radiomics nomogram yielded an AUC of 0.85. PET-MRI based radiomics analysis exhibited effective prediction of the risk of SDM for preoperative PDAC patients and may offer complementary information and provide hints for cancer staging.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints

BackgroundGaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer’s disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints.MethodsA hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances.ResultsPlasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ− subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism – along with elevated plasma neurofilament light chain level – was related to more severe cognitive impairment in Aβ− subjects.ConclusionPlasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost. To generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI. Retrospective. Patients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent 18 F-FDG PET and MRI for determining recurrent brain tumor. 3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, 18 F-FDG PET imaging. Convolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland-Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5-point scale, with score ≥3 as high-quality. They assessed the lesions on a 5-point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET. The agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance. The synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, -31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high-quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall. The proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers. 3 TECHNICAL EFFICACY: Stage 2.

Indeterminate thyroid cytology in an iodine-deficient population: Prevalence, operation rate, risk of malignancy anddiagnostic value of thyroid scintigraphy and 18F-FDG PET imaging parameters.

Patients with indeterminate thyroid cytology often have a diagnostic hemithyroidectomy. The role of molecular imaging, particularly fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), is controversial, and demographic variations in tumor prevalence and histology influence test performance and clinical utility. We retrospectively assessed the prevalence of indeterminate thyroid cytology and the malignancy rate in patients from an iodine deficient area, and evaluated the diagnostic value of preoperative thyroid scintigraphy and 18F-FDG PET/CT among operated patients. From 2006-2018, all patients with indeterminate thyroid cytology from the North Denmark region were included in the study (population 600,000).Clinical data, including operation rate, preoperative molecular imaging, and histopathological diagnosis, were retrieved. The results from preoperative thyroid scintigraphy and 18F-FDG PET/CT-scanning were compared to the final histopathological diagnosis. Four hundred and thirty-three patients were found with indeterminate thyroid cytology.The main registered reasons for conducting a fine needle aspiration biopsy (FNAB) were "cold nodule", goiter or a palpable nodule (n=312). In 40 patients (9%), FNAB was registered as conducted due to a PET incidentaloma. Four hundred and two patients (93%) underwent diagnostic lobectomy, and this population formed the study population for the following explorative study: One-hundred and two patients (25%) had a malignant diagnosis. In 226 (56%) and 51 (25%) patients, respectively, preoperative thyroid scintigraphy and 18F-FDG PET/computed tomography (CT) was performed. Among patients with a final malignant disease who had a thyroid scintigraphy (40 patients), a cold nodule was seen in 90% of cases; one (atypical) patient presented a warm nodule. Among patients with a final malignant disease 16 patients (16%) had a 18F-FDG PET (incl. 3 missing PET scans). Among the 51 patients with preoperative 18F-FDG PET/CT, no difference in the PET-derived parameters was found between benign (n=33) and malignant tumors (n=13). The prevalence of malignancy (25%) was comparable with that in other studies. In patients with a preoperative thyroid scintigraphy, 90% of malignant cases had a cold nodule on scintigraphy. In patients with a preoperative PET/CT-scanning presenting focal 18F-FDG-uptake, PET-derived parameters had no diagnostic value. However, the diagnostic value of 18F-FDG-avidity vs. non-avidity needs to be addressed prospectively.

Hypometabolic and hypermetabolic brain regions in patients with ALS-FTD show distinct patterns of grey and white matter degeneration: A pilot multimodal neuroimaging study

BackgroundUp to 50% of amyotrophic lateral sclerosis (ALS) patients develop some degree of cognitive dysfunction and a small proportion of these develop frontotemporal dementia (FTD). Non-invasive techniques of magnetic resonance imaging (MRI) and [18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography (PET) have demonstrated structural and metabolic abnormalities, respectively, in the brains of such patients with ALS-FTD. Although initial 18F-FDG PET studies in ALS patients showed only hypometabolism of motor and extramotor brain regions, subsequent studies have demonstrated hypermetabolic changes as well. Such contrasting findings prompted us to hypothesize that hypo- and hypermetabolic brain regions in ALS-FTD patients are associated with divergent degeneration of structural grey matter (GM) and white matter (WM). MethodsCerebral glucose metabolic rate (CMRglc), cortical thickness (CT), fractal dimension (FD), and graph theory WM network analyses were performed on clinical MRI and 18F-FDG PET images from 8 ALS-FTD patients and 14 neurologic controls to explore the relationship between GM-WM degeneration and hypo- and hypermetabolic brain regions. ResultsCMRglc revealed significant hypometabolism in frontal and precentral gyrus brain regions, with hypermetabolism in temporal, occipital and cerebellar regions. Cortical thinning was noted in both hypo- and hypermetabolic brain areas. Unlike CT, FD did not reveal widespread GM degeneration in hypo- and hypermetabolic brain regions of ALS-FTD patients. Graph theory analysis showed severe WM degeneration in hypometabolic but not hypermetabolic areas, especially in the right hemisphere. ConclusionOur multimodal MRI-PET study provides insights into potentially differential pathophysiological mechanisms between hypo- and hypermetabolic brain regions of ALS-FTD patients.

Metformin confers longitudinal cardiac protection by preserving mitochondrial homeostasis following myocardial ischemia/reperfusion injury.

Myocardial ischemia-reperfusion (I/R) injury is associated with systemic oxidative stress, cardiac mitochondrial homeostasis, and cardiomyocyte apoptosis. Metformin has been recognized to attenuate cardiomyocyte apoptosis. However, the longitudinal effects and pathomechanism of metformin on the regulation of myocardial mitohormesis following I/R treatment remain unclear. This study aimed to investigate the longitudinal effects and mechanism of metformin in regulating cardiac mitochondrial homeostasis by serial imaging with the 18-kDa translocator protein (TSPO)-targeted positron emission tomography (PET) tracer 18F-FDPA. Myocardial I/R injury was established in Sprague-Dawley rats, which were treated with or without metformin (150mg/kg per day). Serial gated 18F-FDG and 18F-FDPA PET imaging wereperformed at 1, 4, and 8weeks after surgery, followed by analysis of ventricular remodelling and cardiac mitochondrial homeostasis. The correlation between Hsp60 and 18F-FDPA uptake was analyzed. After PET imaging, the activity of antioxidant enzymes, immunostaining, and western blot analysis were performed to analyze the spatio-temporal effects and pathomechanism of metformin for cardiac protection after myocardial I/R injury. Oxidative stress and apoptosis increased 1week after myocardial I/R injury (before significant progression of ventricular remodelling). TSPO expression was correlated with Hsp60 expression and was co-localized with inflammatory CD68+ macrophages in the infarct area, and TSPO uptake was associated with an upregulation of AMPK-p/AMPK and a downregulation of Bcl-2/Bax. However, these effects were reversed with metformin treatment. Eight weeks after myocardial I/R injury (representing the advanced stage of heart failure), 18F-FDPA uptake in myocardial cells in the distal non-infarct area increased without CD68+ expression, whereas the activity decreased with metformin treatment. Taken together, these results show that a prolonged metformin treatment has pleiotropic protective effects against myocardial I/R injury associated with a regional and temporal dynamic balance between mitochondrial homeostasis and cardiac outcome, which were assessed by TSPO-targeted imaging during cardiac remodelling.

PET morphology helps distinguish solitary and solid pulmonary tuberculosis from non-small cell lung cancer.

Solitary and solid pulmonary tuberculosis (PTB) and non-small cell lung cancer (NSCLC) can present overlapping imaging features, causing diagnostic dilemmas. Hence, this study aimed to identify positron emission tomography (PET) morphological features derived from fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images for a better differential diagnosis. Clinical records and 18F-FDG PET/CT images of 175 patients confirmed with PTB and 311 patients with NSCLC were retrospectively reviewed. Parameters including patient demographics, PET-derived morphological features and metabolic parameters, and CT-derived morphological features were investigated. Logistic regression analysis was performed to assess the independent predictive factors associated with PTB. PTB presented with more heterogeneous glucometabolism than NSCLC in PET imaging (50% vs 17%, P < 0.05), especially in lesions with a maximum diameter < 30mm (39% vs. 5%, P < 0.05). NSCLC usually showed centric hypometabolism, whereas PTB more frequently presented with an eccentric metabolic pattern, mainly including piebald, half-side, lesser curvature, and greater curvature shapes. Multivariate logistic regression identified that glucometabolic heterogeneity, eccentric hypometabolism, smaller lesion size, calcification, satellite lesions, and higher CT value of the hypometabolic area were independently diagnostic factors for PTB. Morphological features derived from 18F-FDG PET images helped distinguish solitary and solid PTB from NSCLC.

Comparison between a dual-time-window protocol and other simplified protocols for dynamic total-body 18F-FDG PET imaging

PurposeEfforts have been made both to avoid invasive blood sampling and to shorten the scan duration for dynamic positron emission tomography (PET) imaging. A total-body scanner, such as the uEXPLORER PET/CT, can relieve these challenges through the following features: First, the whole-body coverage allows for noninvasive input function from the aortic arteries; second, with a dramatic increase in sensitivity, image quality can still be maintained at a high level even with a shorter scan duration than usual. We implemented a dual-time-window (DTW) protocol for a dynamic total-body 18F-FDG PET scan to obtain multiple kinetic parameters. The DTW protocol was then compared to several other simplified quantification methods for total-body FDG imaging that were proposed for conventional setup.MethodsThe research included 28 patient scans performed on an uEXPLORER PET/CT. By discarding the corresponding data in the middle of the existing full 60-min dynamic scan, the DTW protocol was simulated. Nonlinear fitting was used to estimate the missing data in the interval. The full input function was obtained from 15 subjects using a hybrid approach with a population-based image-derived input function. Quantification was carried out in three areas: the cerebral cortex, muscle, and tumor lesion. Micro- and macro-kinetic parameters for different scan durations were estimated by assuming an irreversible two-tissue compartment model. The visual performance of parametric images and region of interest-based quantification in several parameters were evaluated. Furthermore, simplified quantification methods (DTW, Patlak, fractional uptake ratio [FUR], and standardized uptake value [SUV]) were compared for similarity to the reference net influx rate Ki.ResultsKi and K1 derived from the DTW protocol showed overall good consistency (P < 0.01) with the reference from the 60-min dynamic scan with 10-min early scan and 5-min late scan (Ki correlation: 0.971, 0.990, and 0.990; K1 correlation: 0.820, 0.940, and 0.975 in the cerebral cortex, muscle, and tumor lesion, respectively). Similar correlationss were found for other micro-parameters. The DTW protocol had the lowest bias relative to standard Ki than any of the quantification methods, followed by FUR and Patlak. SUV had the weakest correlation with Ki. The whole-body Ki and K1 images generated by the DTW protocol were consistent with the reference parametric images.ConclusionsUsing the DTW protocol, the dynamic total-body FDG scan time can be reduced to 15 min while obtaining accurate Ki and K1 quantification and acceptable visual performance in parametric images. However, the trade-off between quantification accuracy and protocol implementation feasibility must be considered in practice. We recommend that the DTW protocol be used when the clinical task requires reliable visual assessment or quantifying multiple micro-parameters; FUR with a hybrid input function may be a more feasible approach to quantifying regional metabolic rate with a known lesion position or organs of interest.

Rapid Standardized CT-Based Method to Determine Lean Body Mass SUV for PET-A Significant Improvement Over Prediction Equations.

In 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) studies, maximum standardized uptake value (SUVmax) is the parameter commonly used to provide a measurement of the metabolic activity of a tumor. SUV normalized by body mass is affected by the proportions of body fat and lean tissue, which present high variability in patients with cancer. SUV corrected by lean body mass (LBM), denoted as SUL, is recommended to provide more accurate, consistent, and reproducible SUV results; however, LBM is frequently estimated rather than measured. Given the increasing importance of a quantitative PET parameter, especially when comparing PET studies over time to evaluate disease response clinically, and its use in oncological clinical trials, we set out to evaluate the commonly used equations originally derived by James (1976) and Janmahasatian et al. (2005) against computerized tomography (CT)-derived measures of LBM.MethodsWhole-body 18F-FDG PET images of 195 adult patients with cancer were analyzed retrospectively. Representative liver SUVmean was normalized by total body mass. SUL was calculated using a quantitative determination of LBM based on the CT component of the PET/CT study (LBMCT) and compared against the equation-estimated SUL. Bland and Altman plots were generated for SUV-SUL differences.ResultsThis consecutive sample of patients undergoing usual care (men, n = 96; women, n = 99) varied in body mass (38–127 kg) and in Body Mass Index (BMI) (14.7–47.2 kg/m2). LBMCT weakly correlated with body mass (men, r2 = 0.32; women, r2 = 0.22), and thus SUV and SULCT were also weakly correlated (men, r2 = 0.24; women, r2 = 0.11). Equations proved inadequate for the assessment of LBM. LBM estimated by James’ equation showed a mean bias (overestimation of LBM compared with LBMCT) in men (+6.13 kg; 95% CI 4.61–7.65) and in women (+6.32 kg; 95% CI 5.26–7.39). Janmahasatian’s equation provided similarly poor performance.ConclusionsCT-based LBM determinations incorporate the patient’s current body composition at the time of a PET/CT study, and the information garnered can provide care teams with information with which to more accurately determine FDG uptake values, allowing comparability over multiple scans and treatment courses and will provide a robust basis for the use of PET Response Criteria in Solid Tumors (PERCIST) in clinical trials.

Imaging CXCR4 receptors expression for staging multiple myeloma by using 68Ga-Pentixafor PET/CT: comparison with 18F-FDG PET/CT.

68Ga-Pentixafor positron emission tomography (PET) imaging targets CXCR4 expression which is overexpressed in multiple myeloma (MM). In this study, we evaluated the diagnostic utility of 68Ga-Pentixafor PET/CT for imaging CXCR4 expression in MM and compared results with 18F-fluorodeoxyglucose (18F-FDG) PET/CT. 34 (21M; 13F; median age = 57.5 years) treatment naive multiple myeloma patients were recruited. All the patients underwent 18F-FDG PET/CT and 68Ga-Pentixafor PET/CT imaging. Freshly prepared 68Ga-Pentixafor (148-185 MBq) was injected intravenously and whole-body PET/CT (low-dose CT) was acquired at 1 h post-injection. The pattern of uptake (diffuse, focal or mixed) and the mean SUVmax value of all the lesions (when lesions were ≤5) or of the five most tracer avid lesions (when lesions was >5) were evaluated. Tumor to background ratio (TBRmax) was calculated for both the tracers. Durie Salmon plus staging (DSPS) was used for disease staging on PET and the results were compared with International staging system (ISS). 68Ga-Pentixafor PET/CT showed higher disease extent than seen on 18F-FDG PET/CT in 23/34 patients (68.0%), lesser disease extent in 2/34 (6%) and similar disease extent in 9/34 (26%) patients. Significantly (p < 0.001) higher TBRmax values (5.7; IQR 8.8) were observed on 68Ga-Pentixafor PET/CT as compared to 18F-FDG PET/CT values (2.9; IQR = 4.0). Both the techniques detected extramedullary lesions in six patients. On the other hand, 68Ga-Pentixafor detected medullary lesions in five, whereas, 18F-FDG PET in three patients. Further, only 68Ga-Pentixafor TBRmax correlated significantly (ρ = 0.421; 0.013) with bone marrow plasma cell percentage. 68Ga-Pentixafor PET upstaged more number (9/29) of patients as compared to (4/29) 18F-FDG PET imaging. On the other hand, 18F-FDG PET down-staged 9/29, whereas 68Ga-Pentixafor PET downstaged only 3/29 patients. 68Ga-Pentixafor PET/CT evaluated the whole-body disease burden of CXCR4 receptors non-invasively which is not possible by tissue sampling methods. This novel PET tracer has also implication for disease staging. Dual 68Ga-Pentixafor/18F-FDG PET/CT imaging may help in determining the tumor heterogeneity in MM. This CXCR4 targeting PET tracer has a promising role in the development of CXCR4 targeting theranostics and also for response assessment to these therapies including the conventional treatment.

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.

Simple SummaryTraining computer-assisted algorithms on medical images, particularly 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) due to its excellent diagnostic accuracy, is difficult, considering small/fragmented samples or privacy concerns. In computer-vision, deep learning-based models, unlike the conventional data augmentation methods, are highly sought after for creating massive medical samples. For this reason, we developed a multi-scale computational framework to generate synthetic 18F-FDG PET images similar to the real ones in different stages of solid tumor growth and angiogenesis. The framework is developed based on the bio-physiological phenomena of FDG radiotracer uptake in solid tumors using a biomathematical model and a generative adversarial network (GAN)-based architecture. The non-invasive augmented 18F-FDG PET images can be used in clinical practice without the need to manage the patient data. In addition, our spatiotemporal mathematical model can calculate the distribution of various radiopharmaceuticals in different tumor-associated vasculatures.No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages.

Application of 18F-FDG brain PET for survival prediction in a rat model of hanging-induced hypoxic brain injury.

Accurate prediction of survival outcomes after hanging is a crucial and challenging issue in comatose survivors. In this preclinical study, we evaluated the potential utility of using brain glucose metabolism as measured by fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) for survival prediction in a rat model of hanging-induced hypoxic brain injury (HBI). HBI was induced by mechanical hanging using Sprague Dawley rats. 18F-FDG brain PET images were acquired in 26 HBI rats three hours post-injury (3h post-injury) and 4 controls. During the 1month follow-up period, HBI rats were further classified as survivors (n = 15) and nonsurvivors (n = 11). Between-group regional (standardized uptake values normalized to the reference whole brain = SUVRWB, cerebellum = SUVRCB, and pons = SUVRpons) and voxel-based analyses were performed. The prognostic value of the SUVR was tested for overall survival (OS). In addition, diffusion-weighted imaging (DWI) was performed in 2 controls and 5 HBI rats (3 survivors, 2 nonsurvivors, 3h post), and an apparent diffusion coefficient (ADC) map was generated. The nonsurvivor group showed a significantly lower SUVRWB, SUVRCB, and SUVRpons of the cerebral cortices than the survivor group (all p < 0.001). Voxel-based comparison also demonstrated significant reduction in the nonsurvivor group compared with the survivor group (family-wise error-corrected p < 0.05). However, there was no significant difference between controls and survivors. Of 3 reference regions, the SUVRpons demonstrated the largest difference between the survivor and nonsurvivor groups. With an optimal cutoff value of 1.12 (AUC 0.952, p < 0.001), the SUVRpons predicted survival outcomes with a sensitivity of 81.8% and specificity of 100%. The OS of the low SUVRpons group was significantly shorter than that the high SUVRpons group (p < 0.001). The mean ADC values of each brain region showed no significant difference according to survival outcomes. These results suggest the potential utility of 18F-FDG brain PET for predicting survival in hanging-induced HBI.

Association between Visceral Adipose Tissue Metabolism and Alzheimer's Disease Pathology.

The visceral adipose tissue (VAT) has been recognized as an endocrine organ, and VAT dysfunction could be a risk factor for Alzheimer’s disease (AD). We aimed to evaluate the association of VAT metabolism with AD pathology. This cross-sectional study included 54 older subjects with cognitive impairment who underwent 2-deoxy-2-[fluorine-18]-fluoro-D-glucose (18F-FDG) torso positron emission tomography (PET) and 18F-florbetaben brain PET. 18F-FDG uptake in VAT on 18F-FDG PET images was used as a marker of VAT metabolism, and subjects were classified into high and low VAT metabolism groups. A voxel-based analysis revealed that the high VAT metabolism group exhibited a significantly higher cerebral amyloid-β (Aβ) burden than the low VAT metabolism group. In the volume-of-interest analysis, multiple linear regression analyses with adjustment for age, sex, and white matter hyperintensity volume revealed that 18F-FDG uptake in VAT was significantly associated with the cerebral Aβ burden (β = 0.359, p = 0.007). In conclusion, VAT metabolism was associated with AD pathology in older subjects. Our findings suggest that VAT dysfunction could contribute to AD development.