Search

Objective: While the American College of Radiology recommends annual screening mammography starting at age 40 years, the US Preventive Services Task Force (USPSTF) recommends that screening mammography in women younger than age 50 years should involve shared- decision making (SDM) between clinicians and patients, considering benefits and potential harms in younger women. Using a nationally representative cross-sectional survey, we aimed to evaluate patient-reported reasons and predictors of screening mammography utilization in this age group., Methods: Respondents aged 40-49 years from the 2018 National Health Interview Survey (NHIS) without a history of breast cancer were included (response rate 64%). Participants reported sociodemographic variables and reasons they did not engage in mammography screening within the last two years. Multiple variable logistic regression analyses were performed to evaluate the association between sociodemographic characteristics and patient-reported screening mammography use, accounting for complex survey sampling design elements., Results: 1,948 women between the ages of 40-49 years were included. Of this group, (758/1948) 46.6% reported receiving a screening mammogram within the last year, and 1196/1948 (61.4%) reported receiving a screening mammogram within the last two years. The most common reasons for not undergoing screening included: "No reason/never thought about it" 744/1948 (38.2%), "Put it off" 343/1948 (17.6%), "Didn't need it" 331/1948 (16.9%), "Doctor didn't order it" 162/1948 (8.3%), and "I'm too young" 63/1948 (5.3%). Multiple variable analyses demonstrated that lack of health insurance was the strongest predictor of mammography non-engagement (p< 0.001)., Conclusion: Deficits in shared- decision-making in women younger than 50 years related to mammography utilization exist. Radiologists may be key in addressing this issue among ambulatory care providers and patients, educating about the benefits and harms of screening younger women, particularly in racial/ethnic minorities and uninsured patients, who experience additional barriers to care and SDM discussions., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Advances in accelerated magnetic resonance imaging (MRI) continue to push the bounds on achievable spatial and temporal resolution while maintaining a clinically acceptable image quality. Validation tools, including numerical simulations, are needed to characterize the repeatability and reproducibility of such methods for use in quantitative imaging applications. We describe the development of a simulation framework for analyzing and optimizing accelerated MRI acquisition and reconstruction techniques used in dynamic contrast enhanced (DCE) breast imaging. The simulation framework, in the form of a digital reference object (DRO), consists of four modules that control different aspects of the simulation, including the appearance and physiological behavior of the breast tissue as well as the MRI acquisition settings, to produce simulated k-space data for a DCE breast exam. The DRO design and functionality are described along with simulation examples provided to show potential applications of the DRO. The included simulation results demonstrate the ability of the DRO to simulate a variety of effects including the creation of simulated lesions, tissue enhancement modeled by the generalized kinetic model, T1-relaxation, fat signal precession and saturation, acquisition SNR, and changes in temporal resolution.

In this work we show that combining physician rules and machine learned rules may improve the performance of a classifier that predicts whether a breast cancer is missed on percutaneous, image-guided breast core needle biopsy (subsequently referred to as "breast core biopsy"). Specifically, we show how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists (subsequently referred to as "our physicians") can guide the search in an inductive logic programming system, and improve the performance of a learned classifier. Our dataset of 890 consecutive benign breast core biopsy results along with corresponding mammographic findings contains 94 cases that were deemed non-definitive by a multidisciplinary panel of physicians, from which 15 were upgraded to malignant disease at surgery. Our goal is to predict upgrade prospectively and avoid surgery in women who do not have breast cancer. Our results, some of which trended toward significance, show evidence that inductive logic programming may produce better results for this task than traditional propositional algorithms with default parameters. Moreover, we show that adding knowledge from our physicians into the learning process may improve the performance of the learned classifier trained only on data.

Liver resection is the current standard treatment for patients with both primary and metastatic liver cancer. The principal causes of morbidity and mortality after liver resection are related to blood loss (typically between 0.5 and 1 L), especially in cases where transfusion is required. Blood transfusions have been correlated with decreased long-term survival, increased risk of perioperative mortality and complications. The goal of this study was to evaluate different designs of a radiofrequency (RF) electrode array for use during liver resection. The purpose of this electrode array is to coagulate a slice of tissue including large vessels before resecting along that plane, thereby significantly reducing blood loss. Finite Element Method models were created to evaluate monopolar and bipolar power application, needle and blade shaped electrodes, as well as different electrode distances. Electric current density, temperature distribution, and coagulation zone sizes were measured. The best performance was achieved with a design of blade shaped electrodes (5 x 0.1 mm cross section) spaced 1.5 cm apart. The electrodes have power applied in bipolar mode to two adjacent electrodes, then switched sequentially in short intervals between electrode pairs to rapidly heat the tissue slice. This device produces a ~1.5 cm wide coagulation zone, with temperatures over 97 masculineC throughout the tissue slice within 3 min, and may facilitate coagulation of large vessels.

The rat forelimb compression model has been used widely to study bone response to mechanical loading. We used strain gages to assess load sharing between the ulna and radius in the forelimb of adult Fisher rats. We used histology and peripheral quantitative computed tomography (pQCT) to quantify ulnar bone formation 12 days after in vivo fatigue loading. Lastly, we developed a finite element model of the ulna to predict the pattern of surface strains during compression. Our findings indicate that at the mid-shaft the ulna carries 65% of the applied compressive force on the forelimb. We observed large variations in fatigue-induced bone formation over the circumference and length of the ulna. Bone formation was greatest 1-2 mm distal to the mid-shaft. At the mid-shaft, we observed woven bone formation that was greatest medially. Finite element analysis indicated a strain pattern consistent with a compression-bending loading mode, with the greatest strains occurring in compression on the medial surface and lesser tensile strains occurring laterally. A peak strain of -5190 microepsilon (for 13.3N forelimb compression) occurred 1-2 mm distal to the mid-shaft. The pattern of bone formation in the longitudinal direction was highly correlated to the predicted peak compressive axial strains at seven cross-sections (r2 = 0.89, p = 0.014). The in-plane pattern of bone formation was poorly correlated to the predicted magnitude of axial strain at 51 periosteal locations (r2 = 0.21, p < 0.001), because the least bone formation was observed where tensile strains were highest. These findings indicate that the magnitude of bone formation after fatigue loading is greatest in regions of high compressive strain.