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Background: Multiple factors are associated with an increased lifetime risk of breast cancer, including inheritance of an abnormal BRCA 1/2 gene, history of lobular carcinoma in situ (LCIS) or atypical hyperplasia, family history of breast cancer or previous chest wall radiation. In 2007, the American Cancer Society released updated guidelines for breast cancer screening based on risk stratification. These guidelines added annual MRI screening to mammography for women with greater than or equal to a 20-5% lifetime risk. Breast MRI screening trials have consistently demonstrated a higher sensitivity of MRI for malignancy compared with mammography, with an additional cancer yield from MRI of approximately 3%. The purpose of this study was to evaluate MRI screening outcomes in women with an increased risk for breast cancer evaluated in an established breast subspecialty clinic within the University of Wisconsin (UW) Hospital and Clinics. Methods: Patients (Pts) were included if they were seen by a UW breast center nurse practitioner, medical or surgical oncologist between 1/1/2007-3/1/2011 with a diagnosis code of: family history of breast or ovarian cancer, genetic susceptibility to malignant neoplasm or genetic carrier, Hodgkin's disease, LCIS, or atypical hyperplasia. Pts with a co -existing diagnosis of invasive breast cancer or ductal carcinoma in situ prior to initial encounter were excluded. Demographic information, breast cancer risk factors, estimated lifetime risk of breast cancer and screening recommendations were abstracted from the medical record. Results of subsequent breast imaging examinations (including breast MRI, diagnostic and screening mammography, and image-guided biopsies) were analyzed with the use of the mammography information system (PenRad). Results: Of 276 women who met the inclusion criteria, 148 underwent at least 1 screening breast MRI. The majority of MRI screened pts were premenopausal (82%) and Caucasian (96.6%) with a mean age of 42.5 (range 20-68) at their initial encounter. Eighty five percent had a first degree relative with breast cancer and 72.3% of pts undergoing MRI screening had a documented lifetime risk of breast cancer of 20% or greater using a validated model. Within this MRI-screened cohort, 18.2% had a known genetic predisposition to breast cancer. Over the time assessed, 307 MRIs were performed in the 148 pts. Biopsy was recommended and performed based on the results of the MRI in 31 of 307 exams (10%). Ten cancers were detected for a positive predictive value based on biopsy performed of 32% and an overall cancer yield of 3.3% (10 of 307 MRI exams). All cancers were stage 0 - II. All pts are currently with no evidence of disease. Conclusion: Breast MRI has a high positive predictive value and cancer yield with an acceptable biopsy rate in a diverse group of high risk women undergoing breast MRI at an academic center outside of a clinical trial. [ABSTRACT FROM AUTHOR]

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.