Feasibility Pilot Outcomes of a Mammography Decision Support and Navigation Intervention for Women With Serious Mental Illness Living in Supportive Housing Settings.

Objective: People with serious mental illness (SMI) experience significant disparities in morbidity and mortality from preventable and treatable medical conditions. Women with SMI have low mammography screening rates. SMI, poverty, and poor access to care can have a significant effect on a woman’s opportunity to learn about and discuss breast cancer screening with health care providers. This study examines the feasibility pilot outcomes of mammography decision support and patient navigation intervention (DSNI) for women with SMI living in supportive housing settings. The primary research question was: Does the DSNI increase knowledge, promote favorable attitudes, and decrease decisional conflict relating to screening mammography? Methods: We developed the intervention with the community using participatory methods. Women (n = 21) with SMI who had not undergone screening mammography in the past year participated in an educational module and decision counseling session and received patient navigation over a 6-month period. We conducted surveys and interviews at baseline and follow-ups to assess mammography decisional conflict. Results: Among study participants, 67% received a mammogram. The mammogram DSNI was feasible and acceptable to women with SMI living in supportive housing settings. From baseline to 1-month follow-up, decisional conflict decreased significantly (P= .01). The patient navigation process resulted in 270 attempted contacts (M= 12.86, SD = 10.61) by study staff (phone calls and emails with patient and/or case manager) and 165 navigation conversations (M= 7.86, SD = 4.84). A barrier to navigation was phone communication, with in-person navigation being more successful. Participants reported they found the intervention helpful and made suggestions for further improvement. Conclusions: The process and outcomes evaluation support the feasibility and acceptability of the mammography DSNI. This project provides initial evidence that an intervention developed with participatory methods can improve cancer screening outcomes in supportive housing programs for people with SMI

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Healthcare technologies and professional vision

This paper presents some details from an observational evaluation of a computer assisted detection tool in mammography. The use of the tool, its strengths and weaknesses, are documented and its impact on reader's 'professional vision' (Goodwin 1994) considered. The paper suggests issues for the design, use and, importantly, evaluation of new technologies in everyday medical work, pointing to general issues concerning trust – users’ perception of the dependability of the evidence generated by such tools and suggesting that evaluations require an emphasis on the complex issue of what technologies afford their users in everyday work

Human-machine diversity in the use of computerised advisory systems: a case study

Computer-based advisory systems form with their users composite, human-machine systems. Redundancy and diversity between the human and the machine are often important for the dependability of such systems. We discuss the modelling approach we applied in a case study. The goal is to assess failure probabilities for the analysis of X-ray films for detecting cancer, performed by a person assisted by a computer-based tool. Differently from most approaches to human reliability assessment, we focus on the effects of failure diversity — or correlation — between humans and machines. We illustrate some of the modelling and prediction problems, especially those caused by the presence of the human component. We show two alternative models, with their pros and cons, and illustrate, via numerical examples and analytically, some interesting and non-intuitive answers to questions about reliability assessment and design choices for human-computer systems

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

Focal Spot, Summer/Fall 2009

https://digitalcommons.wustl.edu/focal_spot_archives/1112/thumbnail.jp

Perceived Racial Discrimination and Nonadherence to Screening Mammography

Objective. We examined whether African American women were as likely as White women to receive the results of a recent mammogram and to self-report results that matched the mammography radiology report (i.e., were adequately communicated). We also sought to determine whether the adequacy of communication was the same for normal and abnormal results. Methods. From a prospective cohort study of mammography screening, we compared self-reported mammogram results, which were collected by telephone interview, to results listed in the radiology record of 411 African American and 734 White women who underwent screening in 5 hospital-based facilities in Connecticut between October 1996 and January 1998. Using multivariate logistic regression, we identified independent predictors of inadequate communication of mammography results. Results. It was significantly more common for African American women to experience inadequate communication of screening mammography results compared with White women, after adjustment for sociodemographic, access-to-care, biomedical, and psychosocial factors. Abnormal mammogram results resulted in inadequate communication for African American women but not White women (PAfrican American women may not be receiving the full benefit of screening mammograms because of inadequate communication of results, particularly when mammography results are abnormal