18F-fluorodeoxyglucose Positron Emission Tomography Imaging Research Articles

Texture Features of 18F-Fluorodeoxyglucose Positron Emission Tomography for Predicting Programmed Death-Ligand-1 Levels in Non-Small Cell Lung Cancer.

Background: Identifying programmed death-ligand-1 (PD-L1) expression is crucial for optimizing treatment strategies involving immune checkpoint inhibitors. However, the role of intratumoral metabolic heterogeneity specifically derived from 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images in predicting PD-L1 expression in patients with newly diagnosed non-small cell lung cancer (NSCLC) remains unexplored. Here, we investigated the association between FDG PET texture features and PD-L1 expression by retrospectively analyzing the data of patients newly diagnosed with NSCLC who underwent FDG PET/CT scans and PD-L1 immunohistochemical staining before treatment. Methods: Patients were categorized based on their tumor proportion scores (TPSs) into negative-, low-, and high-PD-L1 expression groups. We computed the maximum standardized uptake value and 31 texture features for the primary tumor from PET images and compared differences in parameters among the groups. Results: Of the 83 patients, 12, 45, and 26 were assigned to the negative-, low-, and high-PD-L1 expression groups, respectively. Six specific texture features (low gray-level run emphasis, short-run low gray-level emphasis, long-run high gray-level emphasis, low gray-level zone emphasis, high gray-level zone emphasis, and short-zone low gray-level emphasis) helped distinguish among all possible combinations. Conclusions: Our findings revealed that FDG PET texture features are potential imaging biomarkers for predicting PD-L1 expression in patients newly diagnosed with NSCLC.

18F-fluorodeoxyglucose and 18F-sodium fluoride positron emission tomography imaging in assessing early stages of aortic valve degeneration after transcatheter aortic valve implantation.

Transcatheter aortic valve implantation (TAVI) is a standard treatment for severe aortic stenosis, primarily in elderly patients. With an increasing number of procedures and younger patients, understanding the valve degeneration and its risk factors becomes crucial. We aimed to utilize 18F‑sodium fluoride (18F‑NaF) and 18F‑fluorodeoxyglucose (18F‑FDG) positron emission tomography/computed tomography (PET/CT) to evaluate early TAVI valve degeneration. In this prospective study with a prespecified follow‑up protocol, 71 TAVI patients underwent baseline transthoracic and transesophageal echocardiography, and PET/CT with 18F‑NaF and 18F‑FDG. Of these, 31 patients completed 24‑month control examinations, while the others were lost to mortality and the COVID‑19 pandemic. We measured PET tracer activity and compared 18F‑NaF and 18F‑FDG PET/CT uptake at baseline and 24‑month follow‑up. PET/CT and echocardiography data were analyzed for 31 of the 71 enrolled TAVI patients at a median age of 84 years (interquartile range, 80-86). After TAVI, an improvement in the valve function was observed. During follow‑up, the valve function remained stable. PET/CT demonstrated an increase in 18F‑FDG maximal uptake in the inner (tissue‑to‑background ratio, P = 0.009) and outer (P = 0.01) sides of the TAVI valve stent, but no difference in 18F‑NaF maximal activity (inner, P = 0.17; outer, P = 0.57). Twenty‑four months post‑TAVI, an increase in 18F‑FDG uptake, indicative of inflammation, was observed in the valve, while the uptake of the calcification marker (18F‑NaF) remained stable. Theseobservations might suggest early stages of TAVI valve degeneration, although further investigation is required to confirm this interpretation.

Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography.

Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging. Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects. Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI. Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.

GPi-DBS-induced brain metabolic activation in cervical dystonia

BackgroundDeep brain stimulation (DBS) of the globus pallidus interna (GPi) is a highly efficacious treatment for cervical dystonia, but its mechanism of action is not fully understood. Here, we investigate...

Retinal Thinning as a Marker of Disease Severity in Progressive Supranuclear Palsy.

Progressive supranuclear palsy (PSP) involves a variety of visual symptoms that are thought to be partially caused by structural abnormalities of the retina. However, the relationship between retinal structural changes, disease severity, and intracranial alterations remains unknown. We investigated distinct retinal thinning patterns and their relationship with clinical severity and intracranial alterations in a PSP cohort. We enrolled 19 patients with PSP (38 eyes) and 20 age-matched healthy controls (40 eyes). All of the participants underwent peripapillary and macular optical coherence tomography. Brain 11C-2β-carbomethoxy-3β-(4-fluorophenyl) tropane (11C-CFT) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography imaging were also performed in patients with PSP. We investigated the association between retinal thickness changes and clinical features, striatal dopamine transporter availability, and cerebral glucose metabolism. The peripapillary retinal nerve fiber layer (pRNFL) and macula were significantly thinner in patients with PSP than in controls. The thickness of the superior sector of the pRNFL demonstrated a significant negative relationship with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III and Hoehn and Yahr staging scale scores. A significant negative correlation was found between outer inferior macular thickness and disease duration. Outer temporal macular thickness was positively correlated with Montreal Cognitive Assessment scores. In PSP, lower outer temporal macular thickness was also positively correlated with decreased dopamine transporter binding in the caudate. The pRNFL and macular thinning may be candidate markers for monitoring disease severity. Additionally, macular thinning may be an in vivo indicator of nigrostriatal dopaminergic cell degeneration in PSP patients.

Bifunctional Cascading Nanozymes Based on Carbon Dots Promotes Photodynamic Therapy by Regulating Hypoxia and Glycolysis.

Photodynamic therapy (PDT) still faces great challenges with suitable photosensitizers, oxygen supply, and reactive oxygen species (ROS) accumulation, especially in the tumor microenvironment, feathering hypoxia, and high glucose metabolism. Herein, a carbon dots (CDs)-based bifunctional nanosystem (MnZ@Au), acting as photosensitizer and nanozyme with cascading glucose oxidase (GOx)- and catalase (CAT)-like reactivity, was developed for improving hypoxia and regulating glucose metabolism to enhance PDT. The MnZ@Au was constructed using Mn-doped CDs (Mn-CDs) as a core and zeolitic imidazolate framework-8 (ZIF-8) as a shell to form a hybrid (MnZ), followed by anchoring ultrasmall Au nanoparticles (AuNPs) onto the surface of MnZ through the ion exchange and in situ reduction methods. MnZ@Au catalyzed glucose consumption and oxygen generation by cascading GOx- and CAT-like nanozyme reactions, which was further enhanced by its own photothermal properties. In vitro and in vivo studies also confirmed that MnZ@Au greatly improved CDs penetration, promoted ROS accumulation, and enhanced PDT efficacy, leading to efficient tumor growth inhibition in the breast tumor model. Besides, MnZ@Au enabled photoacoustic (PA) imaging to provide a mapping of Mn-CDs distribution and oxygen saturation, showing the real-time catalytic process of MnZ@Au in vivo. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging also validated the decreased glucose uptake in tumors treated by MnZ@Au. Therefore, the integrated design provided a promising strategy to utilize and regulate the tumor microenvironment, promote penetration, enhance PDT, and finally prevent tumor deterioration.

Clinical Response to Tumor Necrosis Factor-α Inhibitor Therapy in the Management of Cardiac Sarcoidosis

Evidence that tumor necrosis factor-α (TNF-α) inhibitors may benefit patients with cardiac sarcoidosis (CS) is limited to small case series and both imaging and clinical outcomes in this population are not well known. This study aimed to evaluate the disease course of patients with CS treated with either infliximab or adalimumab therapy based on serial 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging and clinical outcomes. An institutional CS research database was queried for patients treated with TNF-α inhibitors between 2016 and 2021. Outcomes included (1) change in mean prednisone dose, (2) FDG-PET improvement, and (3) unplanned hospitalizations, advanced heart failure therapies, or death. Our query yielded 31 patients with CS. A total of 13 patients were on infliximab, 15 patients were on adalimumab, and 3 patients were on adalimumab before transitioning to infliximab. Mean prednisone dose decreased between FDG-PET immediately preceding TNF-α and second after TNF-α FDG-PET (18.6±15.7mg to 7.7±12.4mg, p=0.018). A significant decrease was seen in the mean number of segments demonstrating FDG uptake between most recent pre-TNF-α and first after TNF-α inhibitor FDG-PET (mean segments=4.2 vs 3.1, p=0.048). Between earliest pre-TNF-α and first after TNF-α FDG-PET there was a numerical decrease in average myocardial maximum standard uptake values (SUVmax) (4.4 vs 3.1, p=0.18), and the ratio of SUVmax myocardium:SUVmax blood pool (1.9 vs 1.5, p=0.26). Within 36months of initiating TNF-α inhibitor, 4 patients (13%) experienced unplanned cardiovascular hospitalization (median time to hospitalization=12.1months). In conclusion, in patients with CS, TNF-α inhibitor therapy is associated with decreased glucocorticoid use, numerical decrease in cardiac FDG uptake, and minimal cardiac morbidity.

Cortical cerebrovascular and metabolic perturbations in the 5xFAD mouse model of Alzheimer's disease.

The 5xFAD mouse is a popular model of familial Alzheimer's disease (AD) that is characterized by early beta-amyloid (Aβ) deposition and cognitive decrements. Despite numerous studies, the 5xFAD mouse has not been comprehensively phenotyped for vascular and metabolic perturbations over its lifespan. Male and female 5xFAD and wild type (WT) littermates underwent in vivo 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging at 4, 6, and 12 months of age to assess regional glucose metabolism. A separate cohort of mice (4, 8, 12 months) underwent "vessel painting" which labels all cerebral vessels and were analyzed for vascular characteristics such as vessel density, junction density, vessel length, network complexity, number of collaterals, and vessel diameter. With increasing age, vessels on the cortical surface in both 5xFAD and WT mice showed increased vessel length, vessel and junction densities. The number of collateral vessels between the middle cerebral artery (MCA) and the anterior and posterior cerebral arteries decreased with age but collateral diameters were significantly increased only in 5xFAD mice. MCA total vessel length and junction density were decreased in 5xFAD mice compared to WT at 4 months. Analysis of 18F-FDG cortical uptake revealed significant differences between WT and 5xFAD mice spanning 4-12 months. Broadly, 5xFAD males had significantly increased 18F-FDG uptake at 12 months compared to WT mice. In most cortical regions, female 5xFAD mice had reduced 18F-FDG uptake compared to WT across their lifespan. While the 5xFAD mouse exhibits AD-like cognitive deficits as early as 4 months of age that are associated with increasing Aβ deposition, we only found significant differences in cortical vascular features in males, not in females. Interestingly, 5xFAD male and female mice exhibited opposite effects in 18F-FDG uptake. The MCA supplies blood to large portions of the somatosensory cortex and portions of motor and visual cortex and increased vessel length alongside decreased collaterals which coincided with higher metabolic rates in 5xFAD mice. Thus, a potential mismatch between metabolic demand and vascular delivery of nutrients in the face of increasing Aβ deposition could contribute to the progressive cognitive deficits seen in the 5xFAD mouse model.

Development of lung cancer risk prediction models based on F-18 FDG PET images.

We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and to develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET). We retrospectively included 795 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5years of the PET/CT scan were classified into the lung cancer group (n = 136); those who did not were classified into the control group (n = 659). The healthy individuals were then randomly assigned to either the training (n = 585) or validation sets (n = 210). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained for the bilateral lungs. Logistic regression models including clinical factors, SUVR, and MH index were generated to quantify the probability of lung cancer development using a training set. The prediction models were validated using a validation set. The lung SUVR and lung MH index in the lung cancer group were significantly higher than in the control group (p < 0.001 and p < 0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p < 0.001; male: OR 2.08, p = 0.015; BMI: OR 0.93, p = 0.057; current or past smoker: OR 5.60, p < 0.001; lung SUVR: OR 1.13, p < 0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p < 0.001; male: OR 1.87, p = 0.045; BMI: OR 0.93, p = 0.010; current or past smoker: OR 4.78, p < 0.001; lung MH index: OR 1.33, p < 0.001). In the validation data, combined prediction models 1 and 2 exhibited very good discrimination [area under the receiver operator curve (AUC): 0.867 and 0.901, respectively]. The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers to provide information independent of the clinical parameters, related to lung cancer risk.

Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment.

Background and Objectives: To our knowledge, this is the first study that investigated the prognostic value of radiomics features extracted from not only staging 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) images, but also post-induction chemotherapy (ICT) PET/CT images. This study aimed to construct a training model based on radiomics features obtained from PET/CT in a cohort of patients with locally advanced head and neck squamous cell carcinoma treated with ICT, to predict locoregional recurrence, development of distant metastases, and the overall survival, and to extract the most significant radiomics features, which were included in the final model. Materials and Methods: This retrospective study analyzed data of 55 patients. All patients underwent PET/CT at the initial staging and after ICT. Along the classical set of 13 parameters, the original 52 parameters were extracted from each PET/CT study and an additional 52 parameters were generated as a difference between radiomics parameters before and after the ICT. Five machine learning algorithms were tested. Results: The Random Forest algorithm demonstrated the best performance (R2 0.963-0.998) in the majority of datasets. The strongest correlation in the classical dataset was between the time to disease progression and time to death (r = 0.89). Another strong correlation (r ≥ 0.8) was between higher-order texture indices GLRLM_GLNU, GLRLM_SZLGE, and GLRLM_ZLNU and standard PET parameters MTV, TLG, and SUVmax. Patients with a higher numerical expression of GLCM_ContrastVariance, extracted from the delta dataset, had a longer survival and longer time until progression (p = 0.001). Good correlations were observed between Discretized_SUVstd or Discretized_SUVSkewness and time until progression (p = 0.007). Conclusions: Radiomics features extracted from the delta dataset produced the most robust data. Most of the parameters had a positive impact on the prediction of the overall survival and the time until progression. The strongest single parameter was GLCM_ContrastVariance. Discretized_SUVstd or Discretized_SUVSkewness demonstrated a strong correlation with the time until progression.

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.

Right dorsolateral prefrontal cortex volumetric reduction is associated with antidepressant effect of low-dose ketamine infusion: A randomized, double-blind, midazolam-controlled PET-MRI clinical trial

BackgroundEvidence has shown a rapid antidepressant and antisuicidal effects of low-dose ketamine infusion among patients with treatment-resistant depression (TRD) and prominent suicidal ideation (SI). The dorsolateral prefrontal cortex (DLPFC) plays a crucial role in the TRD pathomechanisms. ObjectiveWhether the structural and functional changes of the DLPFC, particularly Brodmann area 46, are associated with the antidepressant and antisuicidal effects of ketamine infusion among such patients is unknown. MethodsWe randomized 48 patients with TRD and SI into groups receiving a single infusion of 0.5 mg/kg ketamine or 0.045 mg/kg midazolam. The Hamilton Depression Rating Scale and the Montgomery–Asberg Depression Rating Scale were used to assess symptoms. Positron emission tomography (PET)–magnetic resonance imaging was conducted prior to infusion and on Day 3 postinfusion. We performed longitudinal voxel-based morphometry (VBM) analysis to evaluate the gray matter (GM) volume changes of the DLPFC. The standardized uptake value ratio (SUVr) of 18F-fluorodeoxyglucose PET images was calculated using the SUV of the cerebellum as a reference region. ResultsThe VBM analysis revealed a small but significant volumetric reduction in the right DLPFC in the ketamine group compared with that in the midazolam group. A greater reduction in depressive symptoms was associated with a smaller decrease in right DLPFC volumes (p = 0.025). However, we found no SUVr changes of the DLPFC between baseline and post-Day 3 ketamine infusion. DiscussionThe optimal modulation of the right DLPFC GM volumes may play an essential role in the antidepressant neuromechanisms of low-dose ketamine.

Realizing 32-time Scan Duration Reduction of 18F-FDG PET Using Deep Learning Model with Image Augmentation

Purpose: 32-time scan duration reduction of 18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) images through the generation of standard scan duration images using a multi-slice cycle-consistent Generative Adversarial Network (cycle-GAN) was studied. Also, the effect of the image augmentation methods on the performance of the cycle-GAN model was evaluated.&#x0D; Materials and Methods: Four subsets of standard and 32-time short scan duration PET image pairs, each contacting image data of 10 patients were used to train and test (80 percent for training and 20 percent for testing) a multi-slice cycle-GAN separately. Another patient’s image data was used as the validation dataset for different training subsets. When training the cycle-GAN model for each subset, two approaches were followed: with and without image augmentation. Common image quality metrics of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Normalized Root Mean Squared Error (NRMSE) were used to assess the generation performance of the cycle-GAN model. Paired sample t-test statistical testing with a confidence interval of 0.95 was used to determine whether the differences between approaches were statistically significant or not.&#x0D; Results: For subsets 1-3, both training approaches improved the image quality of the short scan duration inputs (p &lt; 0.001) while for subset 4 only the training approach with image augmentation was capable of improving the image quality. However, the training approach with image augmentation offered better results than the approach without image augmentation (p &lt; 0.001).&#x0D; Conclusion: Employing the training approach with image augmentation, the cycle-GAN model was capable of improving the image quality of 1/32nd short scan duration images through the generation of synthetic standard scan duration images. In the case of the training approach without image augmentation, except for subset 4, the model trained on all subsets 1-3 was capable of improving the image quality. Image augmentation does indeed improve the performance of the cycle-GAN model, especially in the case of insufficient available training datasets.

Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation

In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool.A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures.High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc.No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82+0.05 with sensitivity 0.87+0.07, specificity 0.77+0.12, and AUC 0.91+0.03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.

Association of 18 F-Fluorodeoxyglucose-Positron Emission Tomography Activity With Angiographic Progression of Disease in Large Vessel Vasculitis.

To assess whether vascular activity seen on 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) scan is associated with angiographic change in large vessel vasculitis (LVV). Patients with LVV were recruited into a prospective cohort. All patients underwent magnetic resonance angiography or computed tomography angiography and FDG-PET imaging. Follow-up imaging using the same imaging modalities was obtained ≥6 months later per a standardized imaging protocol. Arterial damage, defined as stenosis, occlusion, or aneurysm, and corresponding FDG uptake were evaluated in 17 arterial territories. On follow-up, development of new lesions was recorded, and existing lesions were characterized as improved, worsened, or unchanged. A total of 1,091 arterial territories from 70 patients with LVV (38 patients with Takayasu arteritis, 32 patients with giant cell arteritis) were evaluated. Over a median 1.6 years of follow-up, new lesions developed only in 8 arterial territories in 5 patients with Takayasu arteritis. Arterial lesions improved in 16 territories and worsened in 6 territories. Most arterial territories that did not have vascular activity on FDG-PET scan at baseline had no angiographic change over the follow-up period (787 [99%] of 793). Few territories with baseline FDG-PET activity had angiographic change over time (24 [8%] of 298), but of the territories that developed angiographic change, 80% had FDG-PET activity at baseline. Within the same patient, an arterial territory with baseline FDG-PET activity had significantly increased risk for angiographic change compared to a paired arterial territory without FDG-PET activity (odds ratio 19.49 [95% confidence interval 2.44-156.02]; P < 0.01). Concomitant edema and wall thickening further increased risk for angiographic change. Development of angiographic change was infrequent in this cohort of patients with LVV. A lack of baseline FDG-PET activity was strongly associated with stable angiographic disease. In cases of angiographic progression, change was preceded by the presence of FDG-PET activity.

Stress related neural activity: potential mechanism contributing to benefit of physical activity on heart failure incidence

Abstract Background We previously observed that: 1) physical activity (PA) decreases stress-related neural activity (SNA) in a dose-dependent manner and 2) resting SNA predicts incident heart failure (HF). Thus, we hypothesize that PA may decrease the risk of incident HF in part through its beneficial effect on SNA. Methods Self-reported PA data were acquired from Partners Biobank participants. Metabolic equivalent of task-minutes per week (MET*minutes/week) were calculated and participants were categorized according to their adherence to current PA recommendations (&amp;lt;500 and ≥500 MET-min/wk). In a subset with clinically indicated 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging (n=744), SNA was measured as a ratio of amygdalar to ventromedial prefrontal cortical activity. Subsequent HF and baseline CVD risk factor data were obtained using International Classification of Diseases Codes. Kaplan-Meier and Cox regression analyses were used to evaluate the relationship between PA vs HF risk. Mediation analysis was used to test whether PA's impact on SNA significantly mediated the association between PA and HF. Results Among 50,363 participants (median age 59 years [IQR 42, 70], 40% male), 13,850 (11.6%) were diagnosed with HF over a median period of 3.1 years. PA within recommendations was associated with lower risk of subsequent HF (hazard ratio (HR) [95% confidence interval (CI)]: 0.849 [0.760, 0.948], p=0.004, Figure 1) in a model adjusted for CVD risk factors and history of myocardial infarction. As noted previously, higher SNA associated with higher risk of subsequent HF (HR [95% CI]: 1.34 [1.08, 1.66], p=0.007); and PA within guideline recommendations (vs.below) associated with lower SNA (Zscore ± SD: −0.14±0.92 vs 0.02±1, p=0.037). Moreover, mediation analysis shows that exercise reduces HF risk in part via reductions in SNA, accounting for 9% of the total relationship between exercise and HF risk (p&amp;lt;0.05, Figure 2). Conclusion PA associates with reductions in: A) stress-associated neural activity and B) subsequent HF risk. Moreover, the mechanism by which PA reduces HF risk may involve salutary effects on stress-related neural pathways. Funding Acknowledgement Type of funding sources: None.

Prognostic value of quantitative parameters for esophageal squamous cell carcinoma determined by preoperative FDG-PET after trimodal therapy

BackgroundAssociations between tumor metabolic and volumetric parameters determined by preoperative 18F-fluorodeoxyglucose–positron emission tomography and survival in patients with esophageal squamous cell carcinoma who underwent trimodal therapy have not been fully investigated. MethodsWe evaluated relationships between reductions in maximal standardized uptake value, metabolic tumor volume, and total lesion glycolysis in primary tumors on 18F-fluorodeoxyglucose–positron emission tomography images between before and after neoadjuvant chemoradiotherapy and the survival of 120 patients with esophageal squamous cell carcinoma who underwent neoadjuvant chemoradiotherapy followed by surgery. ResultsThe optimal cutoffs of Δ maximal standardized uptake value, Δ metabolic tumor volume, and Δ total lesion glycolysis were defined to statistically yield the largest differences in recurrence-free survival for good and poor positron emission tomography responders to neoadjuvant chemoradiotherapy (cutoffs: 70%, 85%, and 90%, respectively). These cutoff values significantly stratified overall survival (Δ maximal standardized uptake value, P = .004; Δ metabolic tumor volume, P = .001; Δ total lesion glycolysis, P < .0001). Univariate analysis showed that Δ maximal standardized uptake value (hazard ratio, 0.50; 95% confidence interval, 0.32–0.79; P = .003), Δ metabolic tumor volume (hazard ratio, 0.50; 95% confidence interval, 0.31–0.81; P = .004), and Δ total lesion glycolysis (hazard ratio, 0.37; 95% confidence interval, 0.23–0.61; P < .001) were statistically significant for recurrence-free survival. Furthermore, Δ metabolic tumor volume (hazard ratio, 0.45; 95% confidence interval, 0.27–0.76; P = .003) and Δ total lesion glycolysis (hazard ratio, 0.37; 95% confidence interval, 0.22–0.63; P < .001) were independent factors for recurrence-free survival in multivariate analyses that included preoperative and pathological factors. ConclusionTogether with significant pathological prognostic factors, Δ metabolic tumor volume and Δ total lesion glycolysis were valuable for patients with esophageal squamous cell carcinoma who received trimodal therapy. Thus, preoperative 18F-fluorodeoxyglucose–positron emission tomography is a useful and noninvasive diagnostic tool that might facilitate tailoring optimal therapies for locally advanced esophageal squamous cell carcinoma.

Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study.

BackgroundThis study aimed to explore the potential of a combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and magnetic resonance imaging (MRI) to improve predictions of conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). The predictive performances and specific associated biomarkers of these imaging techniques used alone (single-modality imaging) and in combination (dual-modality imaging) were compared.MethodsThis study enrolled 377 patients with MCI and 94 healthy control participants from 2 medical centers. Enrolment was based on the patients’ brain MRI and PET images. Radiomic analysis was performed to evaluate the predictive performance of dual-modality 18F-FDG PET and MRI scans. Regions of interest (ROIs) were determined using an a priori brain atlas. Radiomic features in these ROIs were extracted from the MRI and 18F-FDG PET scan data. These features were either concatenated or used separately to select features and construct Cox regression models for prediction in each modality. Harrell’s concordance index (C-index) was then used to assess the predictive accuracies of the resulting models, and correlations between the MRI and 18F-FDG PET features were evaluated.ResultsThe C-indices for the two test datasets were 0.77 and 0.80 for dual-modality 18F-FDG PET/MRI, 0.75 and 0.73 for single-modality 18F-FDG PET, and 0.74 and 0.76 for single-modality MRI. In addition, there was a significant correlation between the crucial image signatures of the different modalities.ConclusionsThese results indicate the value of imaging features in monitoring the progress of MCI in populations at high risk of developing AD. However, the incremental benefit of combining 18F-FDG PET and MRI is limited, and radiomic analysis of a single modality may yield acceptable predictive results.

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.