Abrupt 90° and 180° changes in stent geometry (particularly in the side branches) cause a high momentum change and thus increased flow separation and mixing, which has significant implications in blood flow characteristics near the wall. By comparison, longer bridging stents provide more gradual changes in momentum, thus allowing blood flow to develop before reaching the target vessel.

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

Objective Prolonged hospitalization due to burn injury results in physical inactivity and muscle weakness. However, how these changes are distributed among body parts is unknown. The aim of this study was to evaluate the degree of body composition changes in different anatomical regions during intensive care unit hospitalization (ICUh). Design Retrospective chart review. Setting Children’s burn hospital. Patients Twenty-four severely burned children admitted to our institution between 2000 and 2015. Interventions All patients underwent a dual-energy x-ray absorptiometry (DEXA) within 2 weeks after injury and 2 weeks before discharge to determine body composition changes. No subject underwent anabolic intervention. We analyzed changes of bone mineral content, bone mineral density, total fat mass, total mass, and total lean mass of the entire body and specifically analyzed the changes between the upper and lower limbs. Measurements and Main Results In the 24 patients, age was 10±5 years, total body surface area burned was 59±17%, time between DEXAs was 34±21 days, and length of stay was 39±24 days. We found a significant (p<0.001) average loss of 3% of lean mass in the whole body; this loss was significantly greater (p<0.001) in the upper extremities (17%) than in the lower extremities (7%). We also observed a remodeling of the fat compartments, with a significant whole-body increase in fat mass (p<0.001) that was greater in the truncal region (p<0.0001) and in the lower limbs (p<0.05). Conclusions ICUh is associated with greater lean mass loss in the upper limbs of burned children. Mobilization programs should include early mobilization of upper limbs to restore upper extremity function.

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