In retrospective studies, 68 Ga-PSMA-11 positron emission tomographic (PET) imaging improves detection of biochemically recurrent prostate cancer compared with conventional imaging. OBJECTIVE To assess 68 Ga-PSMA-11 PET accuracy in a prospective multicenter trial.

To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18 F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. Materials and Methods: Prospective 18 F-FDG PET brain images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. Results: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P , .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.

Dysregulation of pH in solid tumors is a hallmark of cancer. In recent years, the role of altered pH heterogeneity in space, between benign and aggressive tissues, between individual cancer cells, and between subcellular compartments, has been steadily elucidated. Changes in temporal pH-related processes on both fast and slow time scales, including altered kinetics of bicarbonate-CO2 exchange and its effects on pH buffering and gradual, progressive changes driven by changes in metabolism, are further implicated in phenotypic changes observed in cancers. These discoveries have been driven by advances in imaging technologies. This review provides an overview of intra- and extracellular pH alterations in time and space reflected in cancer cells, as well as the available technology to study pH spatiotemporal heterogeneity.

Occult bacterial infections represent a worldwide health problem. Differentiating active bacterial infection from sterile inflammation can be difficult using current imaging tools. Present clinically viable methodologies either detect morphologic changes (CT/ MR), recruitment of immune cells (111In-WBC SPECT), or enhanced glycolytic flux seen in inflammatory cells (18F-FDG PET). However, these strategies are often inadequate to detect bacterial infection and are not specific for living bacteria. Recent approaches have taken advantage of key metabolic differences between prokaryotic and eukaryotic organisms, allowing easier distinction between bacteria and their host. In this report, we exploited one key difference, bacterial cell wall biosynthesis, to detect living bacteria using a positron-labeled D-amino acid. After screening several 14C D-amino acids for their incorporation into E. coli in culture, we identified D-methionine as a probe with outstanding radiopharmaceutical potential. Based on an analogous procedure to that used for L-[methyl-11C]methionine ([11C] L-Met), we developed an enhanced asymmetric synthesis of D-[methyl-11C]methionine ([11C] D-Met), and showed that it can rapidly and selectively differentiate both E. coli and S. aureus infections from sterile inflammation in vivo. We believe that the ease of [11C] D-Met radiosynthesis, coupled with its rapid and specific in vivo bacterial accumulation, make it an attractive radiotracer for infection imaging in clinical practice.

Incorporation of D-amino acids into peptidoglycan is a unique metabolic feature of bacteria. Since D-amino acids are not metabolic substrates in most mammalian tissues, this difference can be exploited to detect living bacteria in vivo. Given the prevalence of D-alanine in peptidoglycan muropeptides, as well as its role in several antibiotic mechanisms, we targeted this amino acid for positron emission tomography (PET) radiotracer development. D-[3-11 C]Alanine and the dipeptide D-[3-11 C]alanyl-Dalanine were synthesized via asymmetric alkylation of glycine-derived Schiff-base precursors with [ 11 C]methyl iodide in the presence of a cinchonidinium phase-transfer catalyst. In cell experiments, both tracers showed accumulation by a wide variety of both Grampositive and Gram-negative pathogens including Staphylococcus aureus and Pseudomonas aeruginosa. In a mouse model of acute bacterial myositis, D-[3-11 C]alanine was accumulated by living microorganisms but was not taken up in areas of sterile inflammation.When compared to existing clinical nuclear imaging tools, specifically 2-deoxy-2-[ 18 F]fluoro-D-glucose and a gallium citrate radiotracer, D-alanine showed more bacteria-specific uptake. Decreased D-[3-11 C]alanine uptake was also observed in antibioticsensitive microbes after antimicrobial therapy, when compared to that in resistant organisms. Finally, prominent uptake of D-[3-11 C]alanine uptake was seen in rodent models of discitis-osteomyelitis and P. aeruginosa pneumonia. These data provide strong justification for clinical translation of D-[3-11 C]alanine to address a number of important human infections.