Extragonadal Germ Cell Tumor with Hepatic Infiltration in a Child: Aberrant Somatostatin Receptors and the Diagnostic Conundrum.

Extragonadal germ cell tumors (GCTs) are challenging to diagnose. We present a case of suprarenal GCT, with hepatic infiltration where differential diagnosis included neuroblastoma and hepatoblastoma. The positive positron emission tomography scan further obfuscated the situation. The diagnosis was clinched by fine-needle aspiration cytology and cell block immunohistochemistry.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging of pediatric neuroblastoma: a multi-omics parameters method to predict MYCN copy number category.

Background: The MYCN copy number category is closely related to the prognosis of neuroblastoma (NB). Therefore, this study aimed to assess the predictive ability of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features for MYCN copy number in NB. Methods: A retrospective analysis was performed on 104 pediatric patients with NB that had been confirmed by pathology. To develop the Bio-omics model (B-model), which incorporated clinical and biological aspects, PET/CT radiographic features, PET quantitative parameters, and significant features with multivariable stepwise logistic regression were preserved. Important radiomics features were identified through least absolute shrinkage and selection operator (LASSO) and univariable analysis. On the basis of radiomics features obtained from PET and CT scans, the radiomics model (R-model) was developed. The significant bio-omics and radiomics features were combined to establish a Multi-omics model (M-model). The above 3 models were established to differentiate MYCN wild from MYCN gain and MYCN amplification (MNA). The calibration curve and receiver operating characteristic (ROC) curve analyses were performed to verify the prediction performance. Post hoc analysis was conducted to compare whether the constructed M-model can distinguish MYCN gain from MNA. Results: The M-model showed excellent predictive performance in differentiating MYCN wild from MYCN gain and MNA, which was better than that of the B-model and R-model [area under the curve (AUC) 0.83, 95% confidence interval (CI): 0.74-0.92 vs. 0.81, 95% CI: 0.72-0.90 and 0.79, 95% CI: 0.69-0.89]. The calibration curve showed that the M-model had the highest reliability. Post hoc analysis revealed the great potential of the M-model in differentiating MYCN gain from MNA (AUC 0.95, 95% CI: 0.89-1). Conclusions: The M-model model based on bio-omics and radiomics features is an effective tool to distinguish MYCN copy number category in pediatric patients with NB.

SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

(18)F-fluorodeoxyglucose uptake by positron emission tomography in patients with IPAH and CTEPH.

Pulmonary arterial hypertension (PAH) is driven by pathologies associated with increased metabolism such as pulmonary revascularization, vasoconstriction and smooth muscle cell proliferation in pulmonary artery wall. 18-fluorodeoxyglucose positron emission tomography (18FDG-PET) is an imaging technique sensitive to glucose metabolism and might be considered as a non-invasive method for diagnosis due to significant role of inflammation in idiopathic pulmonary artery hypertension (IPAH) and chronic thromboembolic pulmonary hypertension (CTEPH). The present study aimed to investigate the role of PET/CT imaging of patients with IPAH and CTEPH as an alternative diagnosis method. Demographic characteristics, FDG uptake in lungs, pulmonary artery and right ventricle (RV) of 17 patients (10 IPAH, 7 CTEPH), and 30 controls were evaluated. PET scanning, 6-min walk test, pro-BNP level, right heart catheterization of patients were performed both at the onsert and after 6-month PAH specific treatment. IPAH and CTEPH patients had significantly higher left lung FDG (p = 0.006), right lung FDG (p = 0.004), right atrial (RA) FDG (p < 0.001) and RV FDG (p < 0.001) uptakes than controls. Positive correlation was detected between the RV FDG uptake and the mean pulmonary artery pressure (mPAP) (r = 0.7, p = 0.012) and between the RA FDG uptake and the right atrial pressure (RAP) (r = 0.5, p = 0.02). Increased RV FDG and RA FDG uptakes predicts the presence of pulmonary hypertension and correlates with mPAP and RAP, respectively, which are important indicators in the prognosis of PAH. Further studies are required whether FDG PET imaging can be used to diagnose or predict the prognosis of pulmonary hypertension.

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

Customizable Porphyrin Platform Enables Folate Receptor PET Imaging Using Copper-64.

Folate receptors including folate receptor α (FRα) are overexpressed in up to 90% of ovarian cancers. Ovarian cancers overexpressing FRα often exhibit high degrees of drug resistance and poor outcomes. A porphyrin chassis has been developed that is readily customizable according to the desired targeting properties. Thus, compound O5 includes a free base porphyrin, two water-solubilizing groups that project above and below the macrocycle plane, and a folate targeting moiety. Compound O5 was synthesized (>95% purity) and exhibited aqueous solubility of at least 0.48 mM (1 mg/mL). Radiolabeling of O5 with 64Cu in HEPES buffer at 37 °C gave a molar activity of 1000 µCi/µg (88 MBq/nmol). [64Cu]Cu-O5 was stable in human serum for 24 h. Cell uptake studies showed 535 ± 12% bound/mg [64Cu]Cu-O5 in FRα-positive IGROV1 cells when incubated at 0.04 nM. Subcellular fractionation showed that most radioactivity was associated with the cytoplasmic (39.4 ± 2.7%) and chromatin-bound nuclear (53.0 ± 4.2%) fractions. In mice bearing IGROV1 xenografts, PET imaging studies showed clear tumor uptake of [64Cu]Cu-O5 from 1 to 24 h post injection with a low degree of liver uptake. The tumor standardized uptake value at 24 h post injection was 0.34 ± 0.16 versus 0.06 ± 0.07 in the blocking group. In summary, [64Cu]Cu-O5 was synthesized at high molar activity, was stable in serum, exhibited high binding to FRα-overexpressing cells with high nuclear translocation, and gave uptake that was clearly visible in mouse tumor xenografts.

Cadherin-17 as a target for the immunoPET of adenocarcinoma.

PURPOSE: Cadherin-17 (CDH17) is a calcium-dependent cell adhesion protein that is overexpressed in several adenocarcinomas, including gastric, colorectal, and pancreatic adenocarcinoma. High levels of CDH17 have been linked to metastatic disease and poor prognoses in patients with these malignancies, fueling interest in the protein as a target for diagnostics and therapeutics. Herein, we report the synthesis, in vitro validation, and in vivo evaluation of a CDH17-targeted 89Zr-labeled immunoPET probe. METHODS: The CDH17-targeting mAb D2101 was modified with an isothiocyanate-bearing derivative of desferrioxamine (DFO) to produce a chelator-bearing immunoconjugate - DFO-D2101 - and flow cytometry and surface plasmon resonance (SPR) were used to interrogate its antigen-binding properties. The immunoconjugate was then radiolabeled with zirconium-89 (t1/2 ~ 3.3 days), and the serum stability and immunoreactive fraction of [89Zr]Zr-DFO-D2101 were determined. Finally, [89Zr]Zr-DFO-D2101's performance was evaluated in a trio of murine models of pancreatic ductal adenocarcinoma (PDAC): subcutaneous, orthotopic, and patient-derived xenografts (PDX). PET images were acquired over the course of 5 days, and terminal biodistribution data were collected after the final imaging time point. RESULTS: DFO-D2101 was produced with a degree of labeling of ~ 1.1 DFO/mAb. Flow cytometry with CDH17-expressing AsPC-1 cells demonstrated that the immunoconjugate binds to its target in a manner similar to its parent mAb, while SPR with recombinant CDH17 revealed that D2101 and DFO-D2101 exhibit nearly identical KD values: 8.2 × 10-9 and 6.7 × 10-9 M, respectively. [89Zr]Zr-DFO-D2101 was produced with a specific activity of 185 MBq/mg (5.0 mCi/mg), remained >80% stable in human serum over the course of 5 days, and boasted an immunoreactive fraction of >0.85. In all three murine models of PDAC, the radioimmunoconjugate yielded high contrast images, with high activity concentrations in tumor tissue and low uptake in non-target organs. Tumoral activity concentrations reached as high as >60 %ID/g in two of the cohorts bearing PDXs. CONCLUSION: Taken together, these data underscore that [89Zr]Zr-DFO-D2101 is a highly promising probe for the non-invasive visualization of CDH17 expression in PDAC. We contend that this radioimmunoconjugate could have a significant impact on the clinical management of patients with both PDAC and gastrointestinal adenocarcinoma, most likely as a theranostic imaging tool in support of CDH17-targeted therapies.

Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization.

In this study, we compared the repeatability and reproducibility of radiomic features obtained from positron emission tomography (PET) images according to the reconstruction algorithm used-advanced reconstruction algorithms, such as HYPER iterative (IT), HYPER deep learning reconstruction (DLR), and HYPER deep progressive reconstruction (DPR), or traditional Ordered Subset Expectation Maximization (OSEM)-to understand the potential variations and implications of using advanced reconstruction techniques in PET-based radiomics. We used a heterogeneous phantom with acrylic spherical beads (4- or 8-mm diameter) filled with 18F. PET images were acquired and reconstructed using OSEM, IT, DLR, and DPR. Original and wavelet radiomic features were calculated using SlicerRadiomics. Radiomic feature repeatability was assessed using the Coefficient of Variance (COV) and intraclass correlation coefficient (ICC), and inter-acquisition time reproducibility was assessed using the concordance correlation coefficient (CCC). For the 4- and 8-mm diameter beads phantom, the proportion of radiomic features with a COV < 10% was equivocal or higher for the advanced reconstruction algorithm than for OSEM. ICC indicated that advanced methods generally outperformed OSEM in repeatability, except for the original features of the 8-mm beads phantom. In the inter-acquisition time reproducibility analysis, the combinations of 3 and 5 min exhibited the highest reproducibility in both phantoms, with IT and DPR showing the highest proportion of radiomic features with CCC > 0.8. Advanced reconstruction methods provided enhanced stability of radiomic features compared with OSEM, suggesting their potential for optimal image reconstruction in PET-based radiomics, offering potential benefits in clinical diagnostics and prognostics.

Reversible complete atrioventricular block caused by aortic bicuspid valve calcification with severe aortic stenosis: a case report.

Background: The aetiology of secondary complete atrioventricular blocks includes ischaemia, cardiac sarcoidosis, electrolyte imbalance, drug use, rheumatic fever, and infections such as Lyme disease and endocarditis. Diagnosis is important since some of these causes are reversible. Although several studies have reported on aortic valve calcification causing complete atrioventricular blocks, no study has described improvement of complete atrioventricular blocks by removal of the calcification. Case summary: A 42-year-old man with syncope had a Mobitz type II atrioventricular block, an alternating bundle branch block, and severe aortic stenosis. We identified a 10 s paroxysmal complete atrioventricular block with pre-syncope and performed pacemaker implantation. Electrocardiography-gated computed tomography confirmed that the calcification had reached the muscular septum. 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) revealed significant FDG uptake with high CT value of calcification in basal interventricular septum. The calcification in the septum was removed carefully, and aortic valve replacement was performed. The atrioventricular conduction capacity improved post-surgery. During the 1-year follow-up, the patient reported dramatic improvement in exercise capacity. We also noted an improvement of <0.1% in the right ventricular pacing burden. Discussion: Complete atrioventricular blocks occur in patients with aortic stenosis accompanied by severe calcification of the aortic valve, which are visualized comprehensively by echocardiography. Electrocardiography-gated computed tomography and FDG-PET enabled detailed evaluation of the extent of calcification and pre- and post-operative tissue inflammation. Hence, we suspected that the calcification in the septum was causing complete atrioventricular block. Moreover, clinicians should recognize that aortic valve calcification with aortic stenosis can cause complete atrioventricular blocks.

Limb arteries involvement assessed by FDG/PET CT at diagnosis of giant cell arteritis and risk of relapse: an observational study.

Introduction Steroids and anti-IL6 biotherapy are highly effective in obtaining remission in patients with giant cell arteritis (GCA) but the risk of relapses remains high. We aimed to identify predictors of relapse in GCA. Methods All consecutive patients admitted with a new diagnosis of GCA - according to the 2022 American College of Rheumatology/EULAR (ACR/EULAR) classification criteria - between May 2011 and May 2022 were eligible for this study. The primary outcome was the GCA relapse rate over the 36-months follow up. Factors associated with the primary outcome and time to first relapse were analyzed. Results One hundred and eight patients (74 [69-81] years, 64.8% women) with a new diagnosis of GCA were studied. GCA was biopsy-proven in 65 (60.2%) cases. Ninety-eight (90.7%) FDG/PET CT scans performed at diagnosis were available for review. All patients received steroids given for 21.0 [18.0-28.5] months, associated with methotrexate (n=1, 0.9%) or tocilizumab (n=2, 1.9%). During a median follow-up of 27.5 [11.4-35.0] months, relapse occurred in 40 (37%) patients. Multivariable Cox regression model, including general signs, gender, aortic wall thickness, FDG uptake in arterial wall and IV steroid pulse as covariates, showed that both general signs (HR 2.0 [1.0-4.0, p<0.05) and FDG uptake in limb arteries (HR 2.7 [1.3-5.5], p<0.01) at diagnosis were associated with GCA relapse. Conclusion FDG uptake in limb arteries at diagnosis is a predictor of relapse in newly diagnosed GCA.

Sociodemographic factors and Child Opportunity Index disparities associated with missed care opportunities in pediatric patients with lymphoma and leukemia referred for FDG-PET/CT.

BACKGROUND: Little data exists on the association of missed care opportunities (MCOs) in children referred for nuclear medicine/nuclear oncology imaging examinations and socioeconomic disparities. OBJECTIVE: To determine the prevalence of MCOs in children with lymphoma/leukemia scheduled for fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and the impact of sociodemographic factors and Child Opportunity Index (COI). MATERIALS AND METHODS: Retrospective analysis of MCOs in children with lymphoma/leukemia scheduled for FDG-PET/CT (2012 to 2022) was performed. In univariate analysis, patient, neighborhood, and appointment data were assessed across MCOs and completed appointments. Logistic regression evaluated independent effects of patient-, neighborhood-, and appointment-level factors with MCOs. Two-sided P-value < .05 was considered statistically significant. RESULTS: In 643 FDG-PET/CT appointments (n = 293 patients; median age 15 years (IQR 11.0-17.0 years); 37.9% female), there were 20 MCOs (3.1%) involving 16 patients. Only 8.2% appointments involved Black/African American non-Hispanic/Latino patients, yet they made up a quarter of total MCOs. Patients living in neighborhoods with very low or low COI experienced significantly higher MCOs versus zip codes with very high COI (6.9% vs. 0.8%; P = 0.02). Logistic regression revealed significantly increased likelihood of MCOs for patients aged 18 to 21 [odds ratio (OR) 4.50; 95% CI 1.53-13.27; P = 0.007], Black/African American non-Hispanic/Latino (OR 3.20; 95% CI 1.08-9.49; P = 0.04), zip codes with very low or low COI (OR 9.60; 95% CI 1.24-74.30; P = 0.03), and unknown insurance status. CONCLUSION: Children with lymphoma/leukemia, living in zip codes with very low or low COI, and who identified as Black/African American non-Hispanic/Latino experienced more MCOs. Our study supports the need to address intersecting sociodemographic, neighborhood, and health system factors that will improve equitable access to necessary healthcare imaging for children.

B-Glycine as a marker for ß cell imaging and ß cell mass evaluation.

PURPOSE: ß cell mass (BCM) and function are essential to the diagnosis and therapy of diabetes. Diabetic patients serve ß cell loss is, and damage of ß cells leads to severe insulin deficiency. Our understanding of the role of BCM in diabetes progression is extremely limited by lacking efficient methods to evaluate BCM in vivo. In vitro methods of labeling islets, including loading of contrast reagent or integration of exogenous biomarker, require artificial manipulation on islets, of which the clinical application is limited. Imaging methods targeting endogenous biomarkers may solve the above problems. However, traditional reagents targeting GLP-1R and VAMT2 result in a high background of adjacent tissues, complicating the identification of pancreatic signals. Here, we report a non-invasive and quantitative imaging technique by using radiolabeled glycine mimics ([18F]FBG, a boron-trifluoride derivative of glycine) to assay islet function and monitor BCM changes in living animals. METHODS: Glycine derivatives, FBG, FBSa, 2Me-FBG, 3Me-FBG, were successfully synthesized and labeled with 18F. Specificity of glycine derivatives were characterized by in vitro experiment. PET imaging and biodistribution studies were performed in animal models carring GLYT over-expressed cells. In vivo evaluation of BCM with [18F]FBG were performed in STZ (streptozocin) induced T1D (type 1 diabetes) models. RESULTS: GLYT responds to excess blood glycine levels and transports glycine into islet cells to maintain the activity of the glycine receptor (GLYR). Best PET imaging condition was 80 min after given a total of 240 ~ 250 nmol imaging reagent (a mixture of [18F]FBG and natural glycine) intravenously. [18F]FBG can detect both endogenous and exogenous islets clearly in vivo. When applied to STZ induced T1D mouse models, total uptake of [18F]FBG in the pancreas exhibited a linear correlation with survival BCM. CONCLUSION: [18F]FBG targeting the endogenous glycine transporter (GLYT), which is highly expressed on islet cells, avoiding extra modification on islet cells. Meanwhile the highly restricted expression pattern of GLYT excluded the background in adjacent tissues. This [18F]FBG-based imaging technique provides a non-invasive method to quantify BCM in vivo, implying a new evaluation index for diabetic assessment.

The Importance of PET Imaging to Understanding Whole-Body Cortisol Metabolism in Alzheimer's Disease.

Excess cortisol is associated with more severe cognitive decline, Alzheimer's disease, and related dementia phenotypes. The intracellular enzyme 11ß-HSD1 regenerates active cortisol from inactive cortisone. In this current issue, high regional brain occupancy of Xanamemtrademark, determined by [11C]TARACT PET imaging of 11ß-HSD1, in cognitively normal individuals and mild cognitive impartment/Alzheimer's disease (AD) patients is presented. In the future, comprehensive kinetic modeling using arterial sampling for occupancy studies, and whole-body PET imaging of 11ß-HSD1 enzyme levels, in combination with stable isotope studies of cortisol metabolism, can provide broad insight into enzyme levels and activity in AD and other relevant diseases.

Diagnostic value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging in pediatric opsoclonus myoclonus ataxia syndrome presenting with neuroblastoma.

BACKGROUND: Early precision diagnosis and effective treatment of opsoclonus myoclonus ataxia syndrome (OMAS) patients presenting with neuroblastoma can prevent serious neurological outcomes. OBJECTIVE: To assess the diagnostic value of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging in pediatric OMAS with neuroblastoma. MATERIALS AND METHODS: A retrospective evaluation of 45 patients diagnosed with OMAS who underwent 18F-FDG PET/CT was performed. A univariate analysis was performed to compare clinical characteristics between OMAS with and without neuroblastoma. Univariate and multivariate logistic regression analyses were applied to identify independent risk factors for OMAS with neuroblastoma and to develop the clinical model. Finally, independent risk factors and PET/CT were fitted to build the combined model for the diagnosis of OMAS with neuroblastoma and presented as a nomogram. Receiver operating characteristic curve, decision curve, and calibration curve analyses were conducted to evaluate the performance of the models. RESULTS: Among 45 patients, 27 were PET/CT-positive, 23/27 lesions were neuroblastoma, and four were false positives. One of the false positive patients was confirmed to be adrenal reactive hyperplasia by postoperative pathology, and the symptoms of OMAS disappeared in the remaining three cases during clinical follow-up. The average maximal standardized uptake value of PET/CT-positive lesions was 2.6. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of PET/CT were 100%, 81.8%, 85.2%, 100%, and 91.1%, respectively. Age at diagnosis, lactate dehydrogenase, and neuron-specific enolase showed statistically significant differences between OMAS with and without neuroblastoma. Lactate dehydrogenase was identified as the independent risk factor to develop the clinical model, and the clinical model demonstrated an area under the curve (AUC) of 0.82 for the diagnosis of OMAS with neuroblastoma, with an AUC as high as 0.91 when combined with PET/CT. The decision curve analysis and calibration curve demonstrated that the nomogram had good consistency and clinical usefulness. CONCLUSION: In patients with OMAS, 18F-FDG PET/CT has a high diagnostic accuracy in detecting tumors of the neuroblastoma, especially when combined with the independent risk factor serum lactate dehydrogenase.

Prognostic Impact of Visceral Adipose Tissue Imaging Parameters in Patients with Cholangiocarcinoma after Surgical Resection.

Visceral adiposity is known to be related to poor prognosis in patients with cholangiocarcinoma; however, the prognostic significance of the qualitative features of adipose tissue in cholangiocarcinoma has yet to be well defined. This study investigated the prognostic impact of adipose tissue imaging parameters reflecting the quantity and qualitative characteristics of subcutaneous (SAT) and visceral (VAT) adipose tissue on recurrence-free survival (RFS) and overall survival (OS) in 94 patients undergoing resection of cholangiocarcinoma. The area, mean computed tomography (CT) attenuation, and mean 2-deoxy-2-[18F]fluoro-D-glucose (FDG) uptake of SAT and VAT on positron emission tomography (PET)/CT for staging work-up were measured, and the relationship of these adipose tissue imaging parameters with clinicopathological factors and survival was assessed. TNM stage, histologic grade, lymphovascular invasion, and the size of cholangiocarcinoma showed positive correlations with adipose tissue imaging parameters. Multivariate survival analysis demonstrated that the visceral-to-subcutaneous adipose tissue area ratio (VSR) (p = 0.024; hazard ratio, 1.718) and mean FDG uptake of VAT (p = 0.033; hazard ratio, 9.781) were significant predictors for RFS, but all of the adipose tissue imaging parameters failed to show statistical significance for predicting OS. In addition to visceral adiposity, FDG uptake of VAT might be a promising prognostic parameter for predicting RFS in patients with cholangiocarcinoma.

8-Bicycloalkyl-CPFPX derivatives as potent and selective tools for in vivo imaging of the A1 adenosine receptor.

Imaging of the A1 adenosine receptor (A1R) by positron emission tomography (PET) with 8-cyclopentyl-3-(3-[18F]fluoropropyl)-1-propyl-xanthine ([18F]CPFPX) has been widely used in preclinical and clinical studies. However, this radioligand suffers from rapid peripheral metabolism and subsequent accumulation of radiometabolites in the vascular compartment. In the present work, we prepared four derivatives of CPFPX by replacement of the cyclopentyl group with norbornane moieties. These derivatives were evaluated by competition binding studies, microsomal stability assays and LC-MS analysis of microsomal metabolites. In addition, the 18F-labeled isotopologue of 8-(1-norbornyl)-3-(3-fluoropropyl)-1-propylxanthine (1-NBX) as the most promising candidate was prepared by radiofluorination of the corresponding tosylate precursor and the resulting radioligand ([18F]1-NBX) was evaluated by permeability assays with Caco-2 cells and in vitro autoradiography in rat brain slices. Our results demonstrate that 1-NBX exhibits significantly improved A1R affinity and selectivity when compared to CPFPX and that it does not give rise to lipophilic metabolites expected to cross the blood-brain-barrier in microsomal assays. Furthermore, [18F]1-NBX showed a high passive permeability (Pc = 6.9 ± 2.9 × 10-5 cm/s) and in vitro autoradiography with this radioligand resulted in a distribution pattern matching A1R expression in the brain. Moreover, a low degree of non-specific binding (5%) was observed. Taken together, these findings identify [18F]1-NBX as a promising candidate for further preclinical evaluation as potential PET tracer for A1R imaging.

Bibliometric Review of Optimization and Image Processing of Positron Emission Tomography (PET) Imaging System between 1981-2022.

BACKGROUND: PET scan stands as a valuable diagnostic tool in nuclear medicine, enabling the observation of metabolic and physiological changes at a molecular level. However, PET scans have a number of drawbacks, such as poor spatial resolution, noisy images, scattered radiation, artifacts, and radiation exposure. These challenges demonstrate the need for optimization in image processing techniques. OBJECTIVES: Our objective is to identify the evolving trends and impacts of publication in this field, as well as the most productive and influential countries, institutions, authors, themes, and articles. METHODS: A bibliometric study was conducted using a comprehensive query string such as "positron emission tomography" AND "image processing" AND optimization to retrieve 1,783 publications from 1981 to 2022 found in the Scopus database related to this field of study. RESULTS: The findings revealed that the most influential country, institution, and authors are from the USA, and the most prevalent theme is TOF PET image reconstruction. CONCLUSION: The increasing trend in publication in the field of optimization of image processing in PET scans would address the challenges in PET scan by reducing radiation exposure, faster scanning speed, as well as enhancing lesion identification.

Impact Of PET/CT On Treatment In Patients With Head And Neck Squamous Cell Carcinoma.

<b><br>Introduction:</b> Although PET/CT is effective for staging HNSCC, its impact on patient management is somewhat controversial. For this reason, we considered it necessary to carry out a study in order to verify whether PET/CT helps to improve the prognosis and treatment in patients. This study was designed to address the impact of PET-FDG imaging when used alongside CT in the staging and therapeutic management of patients with HNSCC.</br> <b><br>Material and methods:</b> Data was collected from 169 patients diagnosed with HNSCC with both CT and PET/CT (performed within a maximum of 30 days of each other). It was evaluated whether discrepancies in the diagnosis of the two imaging tests had impacted the treatment.</br> <b><br>Results:</b> The combined use of CT and PET/CT led to a change in the treatment of 67 patients, who represented 39.7% of the sample. In 27.2% of cases, it entailed a change in the type of treatment which the patient received. In 3.0% of the cases, using both diagnostic tests led to modifications of the therapeutic intention of our patients.</br> <b><br>Conclusions:</b> Using PET/CT in addition to the conventional imaging method in staging resulted in more successful staging and more appropriate therapeutic decision-making.</br>.

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.