This meta-analysis examines whether differences in biomarkers, such as programmed cell death ligand 1 (PD-L1) expression and tumor mutation burden, are associated with improved response to immunotherapy and survival in patients with advanced non–small cell lung cancer (NSCLC).

Conditional averages of the velocity field, subgrid-scale (SGS) stresses and SGS dissipation are calculated using the velocity fields obtained from the DNS of plane channel flow. The detection criteria isolate the coherent turbulent structures that contribute most strongly to the energy transfer between the large, resolved scales and the subgrid, unresolved, ones. Separate averages are computed for forward and backward scatter. The interscale energy transfer is found to be strongly correlated with the presence of the turbulent structures typical of wall-bounded flows: quasi-streamwise and hairpin vortices, sweeps and ejections. In the buffer layer, strong SGS dissipation is observed near lifted shear layers; the forward scatter is associated with ejections, the backscatter with sweeps. Both backward and forward scatter occur in close proximity to longitudinal vortices that form a very shallow angle to the wall. Further away from the solid boundary, in the logarithmic region and beyond, both forward and backward energy transfer are associated prevalently with ejections. Eddy viscosity models do not predict the three-dimensional structure of these events adequately, while scale-similar models reproduce the correlation between the large-scale coherent structures and the SGS events more accurately.

Key Points Question Can multiparametric magnetic resonance imaging (MRI) radiomic profiles be used to predict axillary lymph node metastasis (ALNM) and disease-free survival (DFS) in patients with early-stage breast cancer? Findings In this prognostic study that included 1214 patients, 2 clinical-radiomic nomograms were developed that accurately predicted ALNM and stratified patients into low-risk and high-risk groups for DFS. Meaning In this study, clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.