The early detection of atherosclerotic disease is vital to the effective prevention and management of life-threatening cardiovascular events such as myocardial infarctions and cerebrovascular accidents. Given the potential for positron emission tomography (PET) to visualize atherosclerosis earlier in the disease process than anatomic imaging modalities such as computed tomography (CT), this application of PET imaging has been the focus of intense scientific inquiry. Although 18F-FDG has historically been the most widely studied PET radiotracer in this domain, there is a growing body of evidence that 18F-NaF holds significant diagnostic and prognostic value as well. In this article, we review the existing literature on the application of 18F-FDG and 18F-NaF as PET probes in atherosclerosis and present the findings of original animal and human studies that have examined how well 18F-NaF uptake correlates with vascular calcification and cardiovascular risk.

The rapidly rising prevalence and cost of Alzheimer’s disease (AD) in recent decades has made the imaging of amyloid-β (Aβ) deposits the focus of intense research. Several amyloid imaging probes with purported specificity for Aβ plaques are currently at various stages of FDA approval. However, a number of factors appear to preclude these probes from clinical utilization. As the available “amyloid specific” PET imaging probes have failed to demonstrate diagnostic value and have shown limited utility for monitoring therapeutic interventions in humans, a debate on their significance has emerged. The aim of this review is to identify and discuss critically the scientific issues contributing to the extensive inconsistencies reported in the literature on their purported in vivo amyloid specificity and potential utilization in patients.

PurposeThis systematic review and meta-analysis aimed to quantify the diagnostic performance of pancreatic venous sampling (PVS), selective pancreatic arterial calcium stimulation with hepatic venous sampling (ASVS), and 18F-DOPA positron emission tomography (PET) in diagnosing and localizing focal congenital hyperinsulinism (CHI).ProceduresThis systematic review and meta-analysis was conducted according to the PRISMA statement. PubMed, EMBASE, SCOPUS and Web of Science electronic databases were systematically searched from their inception to November 1, 2011. Using predefined inclusion and exclusion criteria, two blinded reviewers selected articles. Critical appraisal ranked the retrieved articles according to relevance and validity by means of the QUADAS-2 criteria. Pooled data of homogeneous study results estimated the sensitivity, specificity, likelihood ratios and diagnostic odds ratio (DOR).Results18F-DOPA PET was superior in distinguishing focal from diffuse CHI (summary DOR, 73.2) compared to PVS (summary DOR, 23.5) and ASVS (summary DOR, 4.3). Furthermore, it localized focal CHI in the pancreas more accurately than PVS and ASVS (pooled accuracy, 0.82 vs. 0.76, and 0.64, respectively). Important limitations comprised the inclusion of studies with small sample sizes, high probability of bias and heterogeneity among their results. Studies with small sample sizes and high probability of bias tended to overestimate the diagnostic accuracy.ConclusionsThis systematic review and meta-analysis found evidence for the superiority of 18F-DOPA PET in diagnosing and localizing focal CHI in patients requiring surgery for this disease.

IMPORTANCEMost early lung cancers present as pulmonary nodules on imaging, but these can be easily missed on chest radiographs. OBJECTIVE To assess if a novel artificial intelligence (AI) algorithm can help detect pulmonary nodules on radiographs at different levels of detection difficulty. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study included 100 posteroanterior chest radiograph images taken between 2000 and 2010 of adult patients from an ambulatory health care center in Germany and a lung image database in the US. Included images were selected to represent nodules with different levels of detection difficulties (from easy to difficult), and comprised both normal and nonnormal control.EXPOSURES All images were processed with a novel AI algorithm, the AI Rad Companion Chest X-ray. Two thoracic radiologists established the ground truth and 9 test radiologists from Germany and the US independently reviewed all images in 2 sessions (unaided and AI-aided mode) with at least a 1-month washout period. MAIN OUTCOMES AND MEASURESEach test radiologist recorded the presence of 5 findings (pulmonary nodules, atelectasis, consolidation, pneumothorax, and pleural effusion) and their level of confidence for detecting the individual finding on a scale of 1 to 10 (1 representing lowest confidence; 10, highest confidence). The analyzed metrics for nodules included sensitivity, specificity, accuracy, and receiver operating characteristics curve area under the curve (AUC). RESULTSImages from 100 patients were included, with a mean (SD) age of 55 (20) years and including 64 men and 36 women. Mean detection accuracy across the 9 radiologists improved by 6.4% (95% CI, 2.3% to 10.6%) with AI-aided interpretation compared with unaided interpretation. Partial AUCs within the effective interval range of 0 to 0.2 false positive rate improved by 5.6% (95% CI, −1.4% to 12.0%) with AI-aided interpretation. Junior radiologists saw greater improvement in sensitivity for nodule detection with AI-aided interpretation as compared with their senior counterparts (12%; 95% CI, 4% to 19% vs 9%; 95% CI, 1% to 17%) while senior radiologists experienced similar improvement in specificity (4%; 95% CI, −2% to 9%) as compared with junior radiologists (4%; 95% CI, −3% to 5%). CONCLUSIONS AND RELEVANCEIn this diagnostic study, an AI algorithm was associated with improved detection of pulmonary nodules on chest radiographs compared with unaided interpretation for different levels of detection difficulty and for readers with different experience.