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Objective. To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.Methods. A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dualecho steady-state magnetic resonance imaging sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared to radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structures on the larger OAI data set (n = 9,434 knees). We defined subchondral bone length (SBL), a novel shape measure characterizing the extent of overlying cartilage and bone flattening, and examined its relationship with radiographic joint space narrowing (JSN), concurrent pain and disability (according to the Western Ontario and McMaster Universities Osteoarthritis Index), as well as subsequent partial or total knee replacement. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for each outcome were estimated using relative changes in SBL from the OAI data set stratified into quartiles.Results. The mean SBL values for knees with JSN were consistently different from knees without JSN. Greater changes of SBL from baseline were associated with greater pain and disability. For knees with medial or lateral JSN, the ORs for future knee replacement between the lowest and highest quartiles corresponding to SBL changes were 5.68 (95% CI 3.90-8.27) and 7.19 (95% CI 3.71-13.95), respectively.Conclusion. SBL quantified OA status based on JSN severity and shows promise as an imaging marker in predicting clinical and structural OA outcomes.

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After appropriate training, EVD placement can be safely performed by advanced practitioners with an adequate accuracy of placement.

OBJECTIVETraumatic brain injury (TBI) is a leading cause of morbidity and mortality in the US, but the true incidence of TBI is unknown.METHODSThe National Trauma Data Bank National Sample Program (NTDB NSP) was queried for 2007 and 2013, and population-based weighted estimates of TBI-related emergency department (ED) visits, hospitalizations, and deaths were calculated. These data were compared to the 2017 Centers for Disease Control and Prevention (CDC) report on TBI, which used the Healthcare Cost and Utilization Project’s National (“Nationwide” before 2012) Inpatient Sample and National Emergency Department Sample.RESULTSIn the NTDB NSP the incidence of TBI-related ED visits was 59/100,000 in 2007 and 62/100,000 in 2013. However, in the CDC report there were 534/100,000 in 2007 and 787/100,000 in 2013. The CDC estimate for ED visits was 805% higher in 2007 and 1169% higher in 2013. In the NTDB NSP, the incidence of TBI-related deaths was 5/100,000 in 2007 and 4/100,000 in 2013. In the CDC report, the incidence was 18/100,000 in both years. The CDC estimate for deaths was 260% higher in 2007 and 325% higher in 2013.CONCLUSIONSThe databases disagreed widely in their weighted estimates of TBI incidence: CDC estimates were consistently higher than NTDB NSP estimates, by an average of 448%. Although such a discrepancy may be intuitive, this is the first study to quantify the magnitude of disagreement between these databases. Given that research, funding, and policy decisions are made based on these estimates, there is a need for a more accurate estimate of the true national incidence of TBI.

PURPOSE This study aims to conduct a systematic review of the literature to identify biomarkers associated with breast cancer brain metastasis (BCBM). METHODS A systematic search was conducted in PubMed, Embase, Web of Science, and Cochrane for relevant literature up until October 1, 2018. Case reports, conference abstracts, and expert opinions/letters were excluded. Studies were included if they investigated risk factors for BCBM in a cohort of patients with locoregional or metastatic breast cancer of any subtype. RESULTS From the 4866 studies that were screened, 117 were selected for inclusion and review. Twenty-eight unique biomarkers were investigated, of which three (EGFR, Ki-67, and p53) were assessed by more than two authors. In a pooled analysis of 3 studies, EGFR expression was associated with an increased risk of BM (RR 3.48, 95% CI 2.27–5.32, I2=0%, p-interaction = 0.39, n= 571 patients). In a pooled analysis of 5 studies, increased Ki-67 expression was associated with an increased risk of BM (RR 2.91, 95% CI 1.96–4.32, I2=59%, p-interaction = 0.05, n= 1,178). In a pooled analysis of 4 studies, p53 expression was not associated with a statistically significant risk of BM (RR 1.42, 95% CI 0.98–2.06, I2=53%, p-interaction = 0.10, n= 738). CONCLUSION This study summarizes the various biomarkers investigated for a role in breast cancer brain metastasis. Two biomarkers, EGFR and Ki-67 were identified as having a statistically significant increased risk of BCBM while p53 was not found to be statistically significant. Future studies are needed to develop more robust prediction models, as well as evaluate the other biomarkers identified in this study, which could help clinicians identify patients at high risk of breast cancer brain metastasis.

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