Treatment regimens for pediatric Hodgkin lymphoma (HL) depend on accurate staging and treatment response assessment, based on accurate disease distribution and metabolic activity depiction. With the aim of radiation dose reduction, we compared the diagnostic performance of 18F-FDG PET/MRI with a 18F-FDG PET/CT reference standard for staging and response assessment. Methods Twenty-four patients (mean age, 15.4 y; range, 8-19.5 y) with histologically proven HL were prospectively and consecutively recruited in 2015 and 2016, undergoing both 18F-FDG PET/CT and 18F-FDG PET/MRI at initial staging (n = 24) and at response assessment (n = 21). The diagnostic accuracy of 18F-FDG PET/MRI for both nodal and extranodal disease was compared with that of 18F-FDG PET/CT, which was considered the reference standard. Discrepancies were retrospectively classified as perceptual or technical errors, and 18F-FDG PET/MRI and 18F-FDG PET/CT were corrected by removing perceptual error. Agreement with Ann Arbor staging and Deauville grading was also assessed.  https://www.selleckchem.com/products/aticaprant.html  Results For nodal and extranodal sites combined, corrected staging 18F-FDG PET/MRI sensitivity was 100% (95% CI, 96.7%-100%) and specificity was 99.5% (95% CI, 98.3%-99.9%). Corrected response-assessment 18F-FDG PET/MRI sensitivity was 83.3% (95% CI, 36.5%-99.1%) and specificity was 100% (95% CI, 99.2%-100%). Modified Ann Arbor staging agreement between 18F-FDG PET/CT and 18F-FDG PET/MRI was perfect (κ = 1.0, P = 0.000). Deauville grading agreement between 18F-FDG PET/MRI and 18F-FDG PET/CT was excellent (κ = 0.835, P = 0.000). Conclusion 18F-FDG PET/MRI is a promising alternative to 18F-FDG PET/CT for staging and response assessment in children with HL.This article explores basic statistical concepts of clinical trial design and diagnostic testing, or how one starts with a question, formulates it into a hypothesis on which a clinical trial is then built, and integrates it with statistics and probability, such as determining the probability of rejecting the null hypothesis when it is actually true (type I error) and the probability of failing to reject the null hypothesis when it is false (type II error). There are a variety of tests for different types of data, and the appropriate test must be chosen for which the sample data meet the assumptions. Correcting type I error in the presence of multiple testing is needed to control the error's inflation. Within diagnostic testing, identifying false-positive and false-negative results is critical to understanding the performance of a test. These are used to determine the sensitivity and specificity of a test along with the test's negative predictive value and positive predictive value. These quantities, specifically sensitivity and specificity, are used to determine the accuracy of a diagnostic test using receiver-operating-characteristic curves. These concepts are briefly introduced to provide a basic understanding of clinical trial design and analysis, with references to allow the reader to explore various concepts at a more detailed level if desired.PET with small molecules targeting prostate-specific membrane antigen (PSMA) is being adopted as a clinical standard for prostate cancer imaging. In this study, we evaluated changes in uptake on PSMA-targeted PET in men starting abiraterone or enzalutamide. Methods This prospective, single-arm, 2-center, exploratory clinical trial enrolled men with metastatic castration-resistant prostate cancer initiating abiraterone or enzalutamide. Each patient was imaged with 18F-DCFPyL at baseline and within 2-4 mo after starting therapy. Patients were followed for up to 48 mo from enrollment. A central review evaluated baseline and follow-up PET scans, recording change in SUVmax at all disease sites and classifying the pattern of change. Two parameters were derived the δ-percent SUVmax (DPSM) of all lesions and the δ-absolute SUVmax (DASM) of all lesions. Kaplan-Meier curves were used to estimate time to therapy change (TTTC) and overall survival (OS). Results Sixteen evaluable patients were accrued to the study. Median TTTC was 9.6 mo (95% CI, 6.9-14.2), and median OS was 28.6 mo (95% CI, 18.3-not available [NA]). Patients with a mixed-but-predominantly-increased pattern of radiotracer uptake had a shorter TTTC and OS. Men with a low DPSM had a median TTTC of 12.2 mo (95% CI, 11.3-NA) and a median OS of 37.2 mo (95% CI, 28.9-NA), whereas those with a high DPSM had a median TTTC of 6.5 mo (95% CI, 4.6-NA, P = 0.0001) and a median OS of 17.8 mo (95% CI, 13.9-NA, P = 0.02). Men with a low DASM had a median TTTC of 12.2 mo (95% CI, 11.3-NA) and a median OS of NA (95% CI, 37.2 mo-NA), whereas those with a high DASM had a median TTTC of 6.9 mo (95% CI, 6.1-NA, P = 0.003) and a median OS of 17.8 mo (95% CI, 13.9-NA, P = 0.002). Conclusion Findings on PSMA-targeted PET 2-4 mo after initiation of abiraterone or enzalutamide are associated with TTTC and OS. Development of new lesions or increasing intensity of radiotracer uptake at sites of baseline disease are poor prognostic findings suggesting shorter TTTC and OS.The liver is a major metabolic organ that regulates the whole-body metabolic homeostasis and controls hepatocyte proliferation and growth. The ATF/CREB family of transcription factors integrates nutritional and growth signals to the regulation of metabolism and cell growth in the liver, and deregulated ATF/CREB family signaling is implicated in the progression of type 2 diabetes, nonalcoholic fatty liver disease, and cancer. This article focuses on the roles of the ATF/CREB family in the regulation of glucose and lipid metabolism and cell growth and its importance in liver physiology. We also highlight how the disrupted ATF/CREB network contributes to human diseases and discuss the perspectives of therapeutically targeting ATF/CREB members in the clinic.A novel clustering approach identified five subgroups of diabetes with distinct progression trajectories of complications. We hypothesized that these subgroups differ in multiple biomarkers of inflammation. Serum levels of 74 biomarkers of inflammation were measured in 414 individuals with recent adult-onset diabetes from the German Diabetes Study (GDS) allocated to five subgroups based on data-driven cluster analysis. Pairwise differences between subgroups for biomarkers were assessed with generalized linear mixed models before (model 1) and after (model 2) adjustment for the clustering variables. Participants were assigned to five subgroups severe autoimmune diabetes (21%), severe insulin-deficient diabetes (SIDD) (3%), severe insulin-resistant diabetes (SIRD) (9%), mild obesity-related diabetes (32%), and mild age-related diabetes (35%). In model 1, 23 biomarkers showed one or more pairwise differences between subgroups (Bonferroni-corrected P less then 0.0007). Biomarker levels were generally highest in SIRD and lowest in SIDD.