https://www.selleckchem.com/Proteasome.html OBJECTIVES Oesophageal adenocarcinoma has a poor prognosis and relies on multi-modality assessment for accurate nodal staging. The aim of the study was to determine the prognostic significance of nodal concordance between PET/CT and EUS in oesophageal adenocarcinoma. METHODS Consecutive patients with oesophageal adenocarcinoma staged between 2010 and 2016 were included. Groups comprising concordant node-negative (C-ve), discordant (DC), and concordant node-positive (C+ve) patients were analysed. Survival analysis using log-rank tests and Cox proportional hazards model was performed. The primary outcome was overall survival. A p value less then  0.05 was considered statistically significant. RESULTS In total, 310 patients (median age = 66.0; interquartile range 59.5-72.5, males = 264) were included. The median overall survival was 23.0 months (95% confidence intervals (CI) 18.73-27.29). There was a significant difference in overall survival between concordance groups (X2 = 44.91, df = 2, p  less then  0.001).ode staging. • Patients with discordant lymph node staging between imaging modalities represent an intermediate-risk group for overall survival.OBJECTIVES It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain. METHODS We constructed a convolutional Siamese network to associate MRI scans obtained on subjects from the Osteoarthritis Initiative (OAI) with frequent unilateral knee pain comparing the knee with frequent pain to the contralateral knee without pain. The Siamese network architecture enabled pairwise learning of information from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices obtained from simila