Tube voltage in chest and abdomen DR imaging: Which settings return maximal non-prewhitening model observer with eye filter (NPWE) detectability?

Background: The non-prewhitening computational model observer with eye filter (NPWE) has been shown to reasonably predict human observer performance in general radiography and is an appropriate substitute when real clinical trials are not feasible. In this study, the NPWE model observer is used to detect specific tasks (circular designer nodules) ranging between 1 and 30 mm in diameter using chest and abdomen phantom images acquired across the diagnostic energy range (60-125 kVp) with and without an anti-scatter grid.

Purpose: The aim of this study was to derive tube voltage (kVp) settings that return maximal NPWE detectability (d') of designer nodules, for digital radiography (DR) chest and abdomen imaging.

Contemporary provider perspectives on how to address HPV vaccine hesitancy in the US: A qualitative study.

Introduction: Despite over 15 years of real-world data that supports the safety and efficacy of the human papillomavirus (HPV) vaccine, in the United States vaccine hesitancy persists. Many studies have focused on vaccine-hesitant parents, but fewer have examined provider perspectives on how to address HPV vaccine hesitancy.

Methods: Between July 2021-April 2022, we recruited providers in Maryland and the broader Mid-Atlantic region who practiced pediatrics, primary care, family medicine, or adolescent medicine and who provided outpatient care for children ages 10-17.

Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.

This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks.

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (< 1/10th) of the cost of next-generation sequencing techniques.

Differential utilization of the online patient portal for completion of health-related social needs screening during routine gynecologic cancer care.

Background: Telehealth technologies offer efficient ways to deliver health-related social needs (HRSN) screening in cancer care, but these methods may not reach all populations. The authors examined patient characteristics associated with using an online patient portal (OPP) to complete HRSN screening as part of gynecologic cancer care.

Methods: From June 2021 to June 2023, patients in a gynecologic oncology clinic completed validated HRSN screening questions either (1) using the OPP (independently before the visit) or (2) in person (verbally administered by clinic staff).