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Pancreatic neuroendocrine tumor (pancreatic NETs), is an important cause of cancer‐related death worldwide. No study has rigorously explored the impact of ethnicity on pancreatic NETs. We aimed to demonstrate the relationship between ethnicity and the survival of patients with pancreatic NETs. We used the SEER database to identify patients with pancreatic NETs from 2004 to 2013. Kaplan–Meier methods and Cox proportional hazard models were used to evaluate the impact of race on survival in pancreatic NETs patients. A total of 3850 patients were included: 3357 Non‐Blacks, 493 Blacks. We stratified races as “Black” and “White/Other.” Blacks were more likely to be diagnosed with later stages of tumors (P = 0.021). As for the treatment, the access to surgery seemed to be more limited in Blacks than non‐Black patients (P = 0.012). Compared with non‐Black patients, Black patients have worse overall survival (OS) (HR = 1.17, 95% CI: 1.00–1.37, P = 0.046) and pancreatic neuroendocrine tumors specific survival (PNSS) (HR = 1.22, 95% CI: 1.01–1.48, P = 0.044). Multivariate Cox analysis identified that disease extension at the time of diagnosis and surgical status contributed to the ethnical survival disparity. Black patients whose stages at diagnosis were localized had significantly worse OS (HR = 2.09, 95% CI: 1.18–3.71, P = 0.011) and PNSS (HR = 3.79, 95% CI: 1.62–8.82, P = 0.002). As for the patients who did not receive surgery, Blacks also have a worse OS (HR = 1.18, 95% CI: 1.00–1.41, P = 0.045). The Black patients had both worse OS and PNSS compared to non‐Black patients. The restricted utilization of surgery, and the advanced disease extension at the time of diagnosis are the possible contributors to poorer survival of Blacks with pancreatic NETs.

ObjectivesWe aimed to evaluate the global scientific output of gene research of myocardial infarction and explore their hotspots and frontiers from 2001 to 2015, using bibliometric methods.MethodsArticles about the gene research of myocardial infarction between 2001 and 2015 were retrieved from the Web of Science Core Collection (WoSCC). We used the bibliometric method and Citespace V to analyze publication years, journals, countries, institutions, research areas, authors, research hotspots, and trends. We plotted the reference co-citation network, and we used key words to analyze the research hotspots and trends.ResultsWe identified 1,853 publications on gene research of myocardial research from 2001 to 2015, and the annual publication number increased with time. Circulation published the highest number of articles. United States ranked highest in the countries with most publications, and the leading institute was Harvard University. Relevant publications were mainly in the field of Cardiovascular system cardiology. Keywords and references analysis indicated that gene expression, microRNA and young women were the research hotspots, whereas stem cell, chemokine, inflammation and cardiac repair were the frontiers.ConclusionsWe depicted gene research of myocardial infarction overall by bibliometric analysis. Mesenchymal stem cells Therapy, MSCs-derived microRNA and genetic modified MSCs are the latest research frontiers. Related studies may pioneer the future direction of this filed in next few years. Further studies are needed.

Background: Identifying the nerve block region is important for the less experienced operators who are not skilled in ultrasound technology. Therefore, we constructed and shared a dataset of ultrasonic images to explore a method to identify the femoral nerve block region.Methods: Ultrasound images of femoral nerve block were retrospectively collected and marked to establish the dataset. The U-net framework was used for training data and output segmentation of region of interest.The performance of the model was evaluated by Intersection over Union and accuracy. Then the predicted masks were highlighted on the original image to give an intuitive evaluation. Finally, cross validation was used for the whole data to test the robust of the results.Results: We selected 562 ultrasound images as the whole dataset. The training set intersection over union (IoU) was 0.713, the development set IoU is 0.633 and the test set IoU is 0.638. For the single image, the median and upper/lower quartiles of IoU were 0.722 (0.647-0.789), 0.653 (0.586-0.703), 0.644 (0.555-0.735) for the training set, development set and test set respectively. The segmentation accuracy of the test set was 83.9%. For 10-fold cross validation, the median and quartiles of the 10-iteration sum IoUs was 0.656 (0.628-0.672); for accuracy, they were 88.4% (82.1-90.7%). Conclusions:We provided a dataset and trained a model for femoral-nerve region segmentation with U-net, obtaining a satisfactory performance. This technique may have potential clinical application.

Background: Prognosis prediction is indispensable in clinical practice and machine learning has been proved to be helpful. We expected to predict survival of pancreatic neuroendocrine tumors (PNETs) with machine learning, and compared it with the American Joint Committee on Cancer (AJCC) staging system.Methods: Data of PNETs cases were extracted from The Surveillance, Epidemiology, and End Result (SEER) database. Statistic description, multivariate survival analysis and preprocessing were done before machine learning. Four different algorithms (logistic regression (LR), support vector machines (SVM), random forest (RF) and deep learning (DL)) were used to train the model. We used proper imputations to manage missing data in the database and sensitive analysis was performed to evaluate the imputation. The model with the best predictive accuracy was compared with the AJCC staging system using the SEER cases.Results: The four models had similar predictive accuracy with no significant difference existed (p = 0.664). The DL model showed a slightly better predictive accuracy than others (81.6% (± 1.9%)), thus it was used for further comparison with the AJCC staging system and revealed a better performance for PNETs cases in SEER database (Area under receiver operating characteristic curve: 0.87 vs 0.76). The validity of missing data imputation was supported by sensitivity analysis.Conclusions: The models developed with machine learning performed well in survival prediction of PNETs, and the DL model have a better accuracy and specificity than the AJCC staging system in SEER data. The DL model has potential for clinical application but external validation is needed.