Temporal trends in pediatric cancer mortality: rare cancers lag behind more common cancers.

Temporal trends demonstrate improved survival for many types of common pediatric cancer. Studies have not examined improvement in very rare pediatric cancers or compared these improvements to more common cancers. In this cohort study of the Surveillance, Epidemiology, and End Results (SEER) registry, we examined patients from 1975 to 2016 who were 0-19 years of age at the time of diagnosis.

Racial and Ethnic Survival Disparities in Pediatric Oncology Over Time: An Analysis of the SEER Registry.

Background/purpose: Studies have demonstrated existing racial and ethnic disparities in multiple aspects of pediatric oncology. The purpose of this study was to examine how racial and ethnic disparities in mortality among pediatric oncology patients have changed over time. We examined mortality by race and ethnicity over time within the Surveillance, Epidemiology, and End Results (SEER) registry.

Ability of Caprini and Padua risk-assessment models to predict venous thromboembolism in a nationwide Veterans Affairs study.

Objective: Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are two of the most commonly used risk-assessment models (RAMs) to quantify VTE risk.

Prediction of bleeding in patients being considered for venous thromboembolism prophylaxis.

Background: Venous thromboembolism (pulmonary embolism and deep vein thrombosis) is an important preventable cause of in-hospital death. Prophylaxis with low doses of anticoagulants reduces the incidence of venous thromboembolism but can also cause bleeding. It is, therefore, important to stratify the risk of bleeding for hospitalized patients when considering pharmacologic prophylaxis.

Natural Language Processing for Breast Imaging: A Systematic Review.

Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging.

CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.

We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID-19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19-positive images for training.

Ability of Caprini and Padua Risk-Assessment Models to Predict Venous Thromboembolism in a Nationwide Study.

Background: Venous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are the most commonly used risk-assessment models to quantify VTE risk.

Using Big Data to Evaluate the Association between Periodontal Disease and Rheumatoid Arthritis.

An association between periodontal disease and rheumatoid arthritis is believed to exist. Most investigations into a possible relationship have been case-control studies with relatively low sample sizes. The advent of very large clinical repositories has created new opportunities for data-driven research.

Metabolic profiling in personalized medicine: bridging the gap between knowledge and clinical practice in Type 2 diabetes.

Type 2 diabetes mellitus (DM2) is the most commonly diagnosed metabolic disease and its prevalence is expected to increase. Epidemiological studies clearly show excess mortality associated with DM2, as well as an increased risk of DM2-related complications. Advances in personalized medicine would greatly improve patient care in the field of diabetes and other metabolic diseases.